Residual chlorine is a well-monitored water quality parameter in water distribution networks (WDN) to ensure the microbiological stability of the drinking water supply. To overcome the costs of direct chlorine measurement, predictive models are primarily used to complement water quality monitoring in real-world WDN. Traditional predictive models for chlorine in WDN are process-based and involve numerically solving the advective-reactive (AR) partial differential equation (PDE) governing the transport and decay of chlorine in distribution pipes, arguably the most influential components of water quality due to their extensive spatial coverage. Numerically solving the AR PDE involves spatial and temporal discretisation of the system domain, which is computationally intensive. This makes process-based models impractical for real-time monitoring or digital control of chlorine dosing. To address this limitation, we here compare different deep learning models, including feedforward neural networks (FNNs), convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and convolutional LSTM networks (ConvLSTMs), as computationally efficient surrogates for solving the AR PDE governing chlorine dynamics. The ConvLSTM-based models emerge as the best for approximating the numerical solutions of AR PDE, with greater accuracy and the ability to generalise across different pipe lengths and decay rates. The models based on FNNs, CNNs, and LSTMs exhibit limited generalisation across spatial and temporal domains. These models offer a promising alternative to traditional numerical solvers, advancing the development of hybrid models for predicting chlorine dynamics in WDN.

Sensors in water distribution systems (WDSs) generally use fixed-interval sampling, such as once every 15 min; however, whether this sampling frequency meets actual monitoring and modeling needs has not been sufficiently explored. This study continuously collected pressure monitoring data from three locations in a WDS at a 100-Hz sampling frequency for one month. Frequency analysis reveals that the signal is nonstationary, with frequency characteristics varying significantly across different periods of the day. Since the information content (e.g., high-frequency details) conveyed by the signal differs across time, this necessitates time-varying sampling requirements to adequately capture critical data in each period. Based on these findings, an adaptive sampling method (ASM) for WDS monitoring is proposed. Experimental results demonstrate that the proposed ASM achieves a more uniform distribution of reconstruction error (i.e., the discrepancy between the original high-frequency signal and its reconstruction from sampled data) and a lower overall error magnitude compared with the traditional fixed-interval sampling method (FISM). This study systematically investigates hydraulic data sampling in WDSs given pressure conditions unaffected by anomalous variations, such as sudden pipe bursts or extraordinary operational actions (e.g., valve maneuvers or atypical pump operations). The analysis is based on signal characteristics and provides a foundation for the design of intelligent monitoring networks in WDSs.

Large Language Model-based Multi-Agents (LLM-MAs) are emerging systems that manage complex tasks with specialized and coordinated agents. In this paper, we present new perspectives on the integration of LLM-MA systems into enhancing water engineering practices. Water engineering typically involves data integration, analysis, modeling, decision-making, and cross-disciplinary collaboration, which often present significant difficulties. To address these domain-specific complexities, we explore how LLM-MA systems can support advanced operations in water engineering and facilitate them. By pointing out the linguistic capabilities of LLMs and the modular, scalable, and collaborative architecture of LLM-MA systems, we investigate the role of intelligent agents in enabling timely, adaptive, and traceable solutions. Various practical applications were identified, e.g., LLM-MA for pressure drop detection in water distribution networks, flood management, or in their role as potential negotiating agents to find a balanced solution considering differing goals. Our investigation highlights both the capabilities and limitations of LLM-MAs in water engineering and proposes practical recommendations for their effective implementation within the field. This study seeks to develop a foundational framework for understanding how LLM-MAs can shape the future of water engineering processes.

Water and power systems are deeply interdependent, yet they are typically analyzed and optimized separately. Coupling these systems leverages their mutual dependency to enhance overall efficiency and reliability, although it introduces significant modeling complexity, especially when considering the inherent uncertainty in these systems, such as water demands, power loads, and renewable generation. This study proposes Robust Optimization (RO) and adjustable robust optimization (ARO) for the integrated day-ahead scheduling of pump operations and generator dispatch under uncertainty. A novel formulation is introduced to systematically eliminate uncertain equality constraints, which hinders the direct implementation of RO and ARO models. This is done by decomposing the decision vector into dependent and independent variables and substituting the dependent variables to eliminate the equality constraints. A key advantage of the ARO framework is its ability to incorporate real-time information as uncertainty unfolds, enabling adaptive decision-making. By coupling the two systems, the model allows each system to adjust its operation not only based on internal measurements but also in response to the evolving state of the other system. Two case studies demonstrate the method's performance through a Pareto tradeoff between cost optimality and robustness. The results show that robustness can be dramatically improved, compared to deterministic optimization, increasing the constraint satisfaction rates from less than 25% to above 99% with an increase of only 2-5% in operational costs. The results also highlight the superiority of ARO over RO in achieving lower operational costs for comparable levels of robustness. These findings showcase the potential of the proposed method to generate robust, adaptive policies that support the advancement of sustainable and reliable operation of interdependent infrastructure systems.

Intra-system water quality varies across a water distribution system, and large, unexpected shifts in consumer demands can create changes in the spatio-temporal dynamics of water flows and water quality. Exposure to poor water quality leads to negative health outcomes, and households that seek to avoid tap water and use bottled water as an alternative source of water can bear a substantial cost. This research applies a sociotechnical agent-based modeling framework, the COVID-19 social distancing and tap water avoidance (TWA) agent-based model (COST-ABM) to explore how intra-system changes in water quality lead to poor water quality and issues with affordability for households and neighborhoods. COST-ABM simulates the movement of individual agents to and from home, work, and leisure locations under decisions to social-distance and the purchase of bottled water under decisions to avoid tap water. For scenarios of changing demands associated with social distancing, high water age is exacerbated, which leads to TWA. COST-ABM assesses equity based on the total cost of tap and bottled water as a percentage of income for low-income households. COST-ABM is applied for a synthetic case study that was developed to represent Clinton, North Carolina. A synthetic hydraulic model is created using street maps to place pipes and nodes, well locations to place water sources, and building types to determine demands. Household demographics are distributed to represent the population of Clinton based on census data. Results demonstrate spatial changes in water quality that lead to TWA and economic inequities. Emergent results demonstrate that inequities among demographic groups do not emerge, but that inequities emerge across income groups and water quality at nodes. The sociotechnical modeling approach that is developed in this research applies COST-ABM to identify and quantify inequities in drinking water quality and water affordability in piped distribution systems. This work focuses on demand changes associated with pandemic scenarios and can be applied to assess equity in water for other demand shifting scenarios, such as extreme weather events, adoption of decentralized water systems, and working from home.

Water distribution systems (WDSs) and power grids are vital infrastructures that support daily human activities. Substantial research has focused on optimizing the operation of each system individually. However, the power consumption of WDS creates interdependence between these systems, leading to the need for optimizing their conjunctive operation, also known as the optimal water and power flow (OWPF) problem. Combining the WDS optimal operation problem with the optimal power flow (OPF) problem results in a non-convex mixed integer nonlinear programming (MINLP) problem, presenting significant mathematical and computational challenges. Previous studies have used various approximation methods to make the problem convex and obtain feasible solutions. However, these methods often converge to local optima and lack theoretical guarantees of global optimality. Failing to guarantee global optima or provide an optimality gap may result in inconsistent and untrustworthy results for decision makers. This study introduces a tailored solution method for optimizing the conjunctive operation of WDS and power grids. The method uses polyhedral relaxations of the non-convex hydraulic constraints, along with conic relaxations to address nonlinearities in the OPF problem. By using convex relaxations, the method provides optimality gaps for the computed solutions. To validate its effectiveness, the method is tested on two sample applications and its performance is compared with that of an off-the-shelf nonlinear solver.

Hydraulic transients resulting from sudden pump shutdowns or valve closures can induce severe pressure fluctuations, known as water hammer, which compromise the safety and reliability of water distribution systems. Designing effective surge protection devices requires balancing hydraulic performance with economic feasibility, which naturally leads to a multi-objective optimization problem. This study develops an integrated framework that couples Don Wood’s Wave Plan Method for transient flow simulation with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for optimal selection and design of water hammer arrestors. The proposed model simultaneously minimizes total installation cost and a hydraulic penalty function representing deviations in pressure from allowable limits. Decision variables include geometric and operational parameters of different surge protection devices such as air vessels, relief valves, and surge tanks, all constrained by practical hydraulic and physical limits. The resulting Pareto front illustrates the inherent trade-off between cost and reliability, enabling the identification of near-optimal design solutions. This approach provides a comprehensive basis for improving the hydraulic safety of pressurized water systems while maintaining economic efficiency, offering a flexible tool for future optimization and design studies in transient flow management.

Accurate water quality modeling in water distribution systems (WDS) is essential for ensuring safe and reliable drinking water. While numerical solvers such as EPANET provide robust simulations, their computational cost increases substantially for real-time or large-scale applications, particularly when boundary and initial conditions vary over time. Existing Physics-Informed Neural Network (PINN) approaches face limitations in handling such changing conditions, despite their prevalence in real WDS operations. This study focuses on enhancing the adaptability of PINNs for chlorine modeling under diverse and dynamic scenarios. The proposed framework embeds the governing Advection–Reaction (AR) equation into a deep learning architecture and introduces targeted modifications to the formulation of boundary and initial condition losses. Training data are generated using EPANET simulations, and the framework is evaluated under multiple scenarios, including constant and time-varying velocities as well as fixed and dynamic boundary and initial conditions. Results demonstrate that a PINN model explicitly designed for boundary-condition adaptability can accurately reproduce EPANET water quality simulations while reducing computational demands. Key factors influencing performance, such as proper PDE specification, loss balancing, and data preprocessing, are identified. Although the analysis is conducted on a single-pipe testbed to isolate these effects, the findings establish an essential foundation for extending adaptive PINNs to full WDS networks. The primary contribution of this work is the development and demonstration of a PINN architecture capable of reliably adapting to varying boundary and initial conditions, addressing a critical gap in current PINN-based water quality modeling research.

Water distribution systems (WDSs) utilize battery-powered sensors to monitor essential parameters like flow rate and pressure. Limited battery life requires reducing data upload frequencies to conserve energy, potentially compromising real-time monitoring vital for system reliability and performance. This challenge is addressed by leveraging temporal redundancies from daily cycles and spatial redundancies from sensor data correlations, enabling data extrapolation instead of continuous transmission. This study proposes an edge computing-based sensor scheduling method that optimizes data transmission frequency while maintaining high data accuracy, thereby extending sensor longevity without sacrificing monitoring capabilities. The proposed approach uses predictive models to forecast future sensor values over multiple time steps based on existing data redundancies. If the deviation between predicted and actual measurements is within a predefined threshold, data transmission is skipped, reducing sensor power consumption; otherwise, data is transmitted to ensure accuracy. Applied to a realistic WDS sensor network, the method achieved up to a 75% reduction in sensor energy consumption with 48 estimation steps and a 0.5 m error threshold, while maintaining a relative data error of only 0.7%. These results demonstrate the method's effectiveness in balancing energy savings with data reliability, suggesting a viable solution for enhancing WDS sustainability and efficiency.

This study provides a practice-oriented sensitivity analysis of DeePC for pressure management in water distribution systems. Two public benchmark systems were used, Fossolo (simpler) and Modena (more complex). Each run fixed a monitored node and pressure reference, applied the same randomized identification phase followed by closed-loop control, and quantified performance by the mean absolute error (MAE) of the node pressure relative to the reference value. To better characterize closed-loop behavior beyond MAE, we additionally report (i) the maximum deviation from the reference over the control window and (ii) a valve actuation effort metric, normalized to enable fair comparison across different numbers of valves and, where relevant, different control update rates. Motivated by the need for practical guidance on how hydraulic boundary conditions and algorithmic choices shape DeePC performance in complex water networks, we examined four factors: (1) placement of an additional internal PRV, supplementing the reservoir-outlet PRVs; (2) the control time step (Formula presented.) ; (3) a uniform reservoir-head offset (Formula presented.) ; and (4) DeePC regularization weights (Formula presented.). Results show strong location sensitivity, in Fossolo, topologically closer placements tended to lower MAE, with exceptions; the baseline MAE with only the inlet PRV was 3.35 [m], defined as a DeePC run with no additions, no extra valve, and no changes to reservoir head, time step, or regularization weights. Several added-valve locations improved the MAE (i.e., reduced it) below this level, whereas poor choices increased the error up to ~8.5 [m]. In Modena, 54 candidate pipes were tested, the baseline MAE was 2.19 [m], and the best candidate (Pipe 312) achieved 2.02 [m], while pipes adjacent to the monitored node did not outperform the baseline. Decreasing (Formula presented.) across nine tested values consistently reduced MAE, with an approximately linear trend over the tested range, maximum deviation was unchanged (7.8 [m]) across all (Formula presented.) cases, and actuation effort decreased with shorter steps after normalization. Changing reservoir head had a pronounced effect: positive offsets improved tracking toward a floor of ≈0.49 [m] around (Formula presented.) ≈ +30 [m], whereas negative offsets (below the reference) degraded performance. Tuning of regularization weights produced a modest spread (≈0.1 [m]) relative to other factors, and the best tested combination (λy, λg, λu) = (10², 10⁻³, 10⁻²) yielded MAE ≈ 2.11 [m], while actuation effort was more sensitive to the regularization choice than MAE/max deviation. We conclude that baseline system calibration, especially reservoir heads, is essential before running DeePC to avoid biased or artificially bounded outcomes, and that for large systems an external optimization (e.g., a genetic-algorithm search) is advisable to identify beneficial PRV locations.

The rapid digital transformation of Water Distribution Networks (WDNs) has led to the collection of multi-sensor time series with high temporal and spatial resolution. However, missing data poses a significant challenge, undermining the usability and effectiveness of data-driven applications. Performing missing data imputation is essential to enhance data quality and support intelligent management. This study first reveals that WDN sensor data in tensor form inherently exhibit spatiotemporal redundancy across three dimensions: inter-sensor similarity, intra-day regularity, and daily recurrence. The redundancy can be algebraically characterized by the low-rank structure of WDN tensor data, providing a robust foundation for imputation. Based on these findings, a novel Low-rank Autoregressive Tensor Completion (LATC) approach is proposed to efficiently impute spatiotemporal WDN data. The LATC combines autoregressive regularization with standard low-rank tensor completion, effectively capturing both global redundancy and local correlation of multi-sensor WDN data. Finally, the LATC is validated on four real-world and simulated WDN data sets under eight different missing scenarios. Extensive experiments show that the LATC significantly outperforms state-of-the-art baseline methods, achieving accurate imputation even under severe corruption and complex missing patterns.

Water distribution systems (WDSs) should deliver safe and affordable water for communities, yet consumers are regularly exposed to tap water that violates federal guidelines for pathogens and chemicals. In response to reduced water quality, consumers may shift demands away from tap water to bottled water, increasing household water spending. Intra-system water quality fluctuates with changes in demands, a consequence observed during the COVID-19 pandemic. This research develops the COVID-19 social-distancing and tap water avoidance agent-based model (COST-ABM), which simulates water quality in a water distribution network and decisions to avoid tap water and purchase bottled water. COST-ABM is developed to assess equitable access to affordable water. Agents represent water consumers that decide to avoid tap water by purchasing bottled water for cooking, cleaning, and hygienic end uses, reducing demand from the system. The agent-based model is tightly coupled with a water distribution system model that calculates the spatiotemporal dynamics of water quality in a pipe network, which is used in agent decision-making. Equity is evaluated in a bottom-up approach using the cost of tap and bottled water as a percentage of household income, calculated at each household. The framework is applied for a virtual water distribution system, and results demonstrate economic inequities in water affordability. This research presents a framework to assess equity in a WDS based on tap water avoidance and water affordability and can be used to facilitate infrastructure management that provides equitable access to safe and affordable water.

Water distribution systems (WDSs) exhibit intricate, nonlinear behaviors shaped by both internal dynamics and external influences. The incorporation of additional models, such as contamination or population models, further increases their complexity. This study investigated WDSs under various uncertainty scenarios to enhance system stability, robustness, and control. In particular, we built upon prior research by exploring an Agent-Based Modeling (ABM) framework integrated within a WDS, focusing on three types of uncertainties: (1) adjustments to existing probabilistic parameters, (2) variations in agent movement across network nodes, and (3) changes in agent distributions across different node types. We conducted our analysis using the virtual city of Micropolis as a testbed. Our findings indicate that while the system remains resilient to uncertainties in predefined probabilistic parameters, substantial and often nonlinear effects arise when uncertainties are introduced in agent mobility and distribution patterns. These results emphasize the significance of understanding how WDSs respond to external behavioral dynamics, which is essential for managing real-world challenges, such as pandemics or shifts in urban behavior. This study underscores the necessity for further research into broader uncertainty categories and emergent effects to enhance WDS modeling and inform decision-making.

The inherent uncertainty in water demand poses significant challenges to water distribution systems' (WDSs) efficiency and quality. This study introduces a model predictive control framework tailored for real-time optimal operation of WDSs under uncertain demand and maximum water age constraints to ensure water quality requirements. The methodology presented utilizes a scenario-based energy-cost optimization approach to account for demand uncertainties. As water age is unmeasurable, a model linearly related to some measured/observed network variables (e.g., flows, water levels, etc.) is proposed to infer water age values. Then, a scenario-based mixed-integer linear programming problem is formulated and solved repeatedly online to adjust operational strategies for minimizing energy operation costs while satisfying water age constraints. The outcome of this model is a feasible operation scheme for all demand scenarios, providing a cost-effective decision that meets water age limits. The proposed model is tested on a real-world-based test case and validated through a series of sensitivity analyses.

This research addresses the integrated management of Water Distribution Systems (WDS) and Power Distribution Systems (PDS) to enhance operational efficiency and resilience during extreme scenarios. Traditionally, these systems are operated independently, leading to sub-optimal performance. Alternatively, some studies have proposed a joint operation of WDS and PDS, assuming that decisions are made simultaneously by a single decision-maker, which is impractical. We propose a novel emergency control method that relies on sparse communication between WDS and PDS operators, aiming to minimize load shedding (LS) in the PDS by strategically managing power demand in the interconnected WDS. The results show that the proposed coordinated approach has similar performance to a full cooperation approach between the two systems while requiring only minimal information exchange. Our approach demonstrates the potential of limited yet targeted information exchange, fostering cross-sectoral collaboration to maintain service continuity under extreme conditions, and underscores the critical role of integrated management in ensuring the resilience of interconnected infrastructure systems.

Accurate modeling of pollutant transport during storm events is critical for watershed management and pollution mitigation. This study extends Diskin’s Cell Model, originally developed for rainfall–runoff simulations, to incorporate pollutant transport dynamics. By integrating an Instantaneous Unit Hydrograph (IUH), the model transforms pollutant loads into effective mass transport predictions while ensuring mass conservation. The framework accounts for contamination mobilized by rainfall, including agricultural runoff and industrial discharges, and applies convolution-based routing to capture pollutant dispersion. Calibrations using single-cell, two-cell, and fifteen-cell watersheds validate the model’s predictive capability and demonstrate its effectiveness in estimating pollutant accumulation at downstream locations. The results highlight the model’s potential for scalable water quality assessments, stormwater pollution control, and data-driven watershed management strategies.

Water scarcity presents an ever-growing challenge in global agriculture, with major implications for food security. In the USA, the scale and complexity of the agricultural system magnify these challenges, calling for an integrated and adaptive approach to water management. Hence, we reviewed six key strategies aimed at sustainable agricultural water management — crop distribution optimization, soil management, modern irrigation technologies, water treatment and reuse, reduction of water demand in animal agriculture, and minimizing food loss and waste — identified based on their prominence in recent literature and potential to address water scarcity. In examining these strategies through a multidimensional lens, several challenges have emerged, including gaps in the current structure of incentives, psychological barriers, lack of awareness, reluctance to alter existing farming practices and consumption habits, and insufficient data on the effectiveness of certain water conservation measures. By offering actionable insights into potential areas of improvement, this Review aims to contribute to the ongoing discourse on agricultural sustainability amid changing climate dynamics.

Recent developments in control theory, coupled with the growing availability of real-time data, have paved the way for improved data-driven control methodologies. This study explores the application of the Data-Enabled Predictive Control (DeePC) algorithm to optimize the operation of water distribution systems (WDS). WDS are characterized by inherent uncertainties and complex nonlinear dynamics. Hence, classic control strategies involving physical model-based or state-space methods are often difficult to implement and scale. The DeePC method suggests a paradigm shift by utilizing a data-driven approach. The technique employs a finite set of input-output samples (control settings and measured data) to learn an unknown system's behavior and derive optimal policies, effectively bypassing the need for an explicit mathematical model of the system. In this study, DeePC is applied to two WDS control applications of pressure management and chlorine disinfection scheduling, demonstrating superior performance compared to standard control strategies.

One key aspect of ensuring water safety in water distribution systems (WDS) is the controlled use of disinfectants like chlorine within these systems. The number of disinfectant levels in WDS directly impacts the quality and safety of the water supplied to consumers, thus chlorine/disinfectant regulation in WDS is paramount. An upper residual chlorine limit controls the formation of disinfection byproducts, whereas a lower residual chlorine limit guarantees that the water remains free of organic contaminants. However, accurately modeling the chlorine reaction in WDS is a complex task due to various influencing factors, including pipe material, pipe age, water pH, temperature, and more. The variability in the chlorine reaction rate in WDS poses a significant challenge in accurately predicting water quality provided to the consumers and also affects the optimal scheduling of chlorine booster injections. To ensure the water quality remains within the acceptable range, we consider the chlorine reaction rate as an uncertain parameter and propose an approximate robust reformulation approach for the booster chlorination scheduling problem. We utilize two benchmark WDS systems to perform rigorous testing and analysis of our methodology. The proposed approach provides a systematic and robust method to obtain chlorine injection scheduling that adheres to predefined aims to maintain safe water quality levels while considering the uncertain reaction rate coefficients to be within ellipsoidal uncertainty sets.

Water distribution systems (WDS) and power distribution systems (PDS) are strongly connected and mutually affect each other. Recently, several studies suggested accounting for this dependency to enhance the overall efficiency of the two critical systems. However, in these studies, the uncertainty inherent in the two systems got less attention. Here, we emphasize the uncertainty modeling by using robust optimization (RO) and adjustable robust optimization (ARO) to co-optimize the day-ahead scheduling of an integrated PDS-WDS. In this problem, we consider the uncertainty in both power loads and water demands and its impact on the operational strategies of the coupled system. Using RO and ARO, we can derive the Pareto frontier between operation cost and system reliability. Our results suggest that for high levels of reliability, the ARO outperforms the RO approach, as it can achieve a high level of reliability with lower operation costs.

The operation and management of water distribution systems rely on water metering devices installed throughout the network. However, the rapid urban development and population growth and the relatively high cost of meters can hinder water providers from uniformly collecting water demand data across the network for billing, operational, or maintenance purposes. As a result, predictive methods are required to understand consumption patterns in areas with missing or malfunctioning metering infrastructure by utilizing information from similar, measured areas to bridge this gap in demand data. This study presents a two-step method to partition a water network into similar demand areas in order to predict demand conditions in unmeasured regions. The clustering step employs network topology and hydraulic characteristics to minimize demand variation across clusters. Next, a data-driven model is trained using demand data from multiple clusters to forecast demand in an unmetered cluster outside of the train set. Our two-step method reports R² values as high as 0.90 when trained on a set of 10 clusters from a benchmark hydraulic network. The developed predictive model for unmetered areas offers water providers an alternative approach to understanding demand dynamics across the entire network.

The distribution of clean water through public systems can be inequitable, as variations in water quality are common drivers for negative health outcomes and can lead households to spend more on water treatment or alternative sources of water, such as bottled water. The COVID-19 pandemic caused complex, location dependent changes to demands due to social distancing that led to changes in water quality. The first wave of social distancing was characterized by wide-spread adoption of work-from-home practices and social distancing, causing water demands to shift from places of employment and recreation to residences. Subsequent changes have ushered in a new post-pandemic regime that is characterized by a hybrid work force. The hybrid work force includes many individuals who have adopted a personal schedule of working from home and working from the office that increases the uncertainty in modeling daily population movement changes and demands. The changes in water quality that followed the change from pre-pandemic, business-as-usual scenarios to COVID-19 social distancing scenarios were modeled using an agent-based modeling framework coupled with a virtual WDS network, but further research is needed to explore how changes to hybrid work generate changes in demands and water quality. Water quality changes caused by the transition from pandemic to post-pandemic regimes have not been quantified, and this research develops a tool to test and compare water quality, equitable access to clean water, and the cost of water in pre-pandemic, pandemic, and post-pandemic scenarios. An agent-based model (ABM) is developed to simulate the movement of individual agents to and from home, work, and leisure locations. The cost of buying water is calculated using the demand specified in the hydraulic network. Equity is assessed using the cost of water as a percentage of income for households in the lower 20% of incomes. In this work, an ABM is applied to a hydraulic system synthesized for Clinton, North Carolina. The hydraulic model is created using street maps to place pipes and nodes, well locations to place water sources, and census data to determine the demand required. Household incomes are distributed to represent Clinton, North Carolina, using census data. The ABM is applied for three scenarios, pre-pandemic, pandemic, and post-pandemic, and results demonstrate changes in water quality that lead to economic inequities. The modeling framework that is developed in this research can be applied to assess equity impacts of water quality changes such as those associated with pandemic scenarios.

Modeling water quality in water distribution systems (WDSs) is critical for ensuring safe drinking water and managing contamination risks. Traditional tools, such as EPANET, rely on numerical methods to simulate water quality dynamics, but face challenges in adaptability. Physics-informed neural networks (PINNs), a novel machine learning approach, embed governing equations directly into neural network architecture, enabling efficient simulations with minimal data. This paper presents a comparative analysis of EPANET and PINNs in modeling chlorine transport in a simplified WDS network. The study assumes constant chlorine concentration at the source, with no reactive losses, and evaluates the ability of PINNs to replicate EPANET results. While PINNs demonstrated consistency with EPANET simulations, the simplified nature of the case study limits broader conclusions about their performance in complex systems. Nevertheless, the study highlights PINNs as a promising framework for future applications in water quality modeling, with potential benefits such as enhanced resolution and flexibility for spatiotemporal analysis. Future research should focus on applying PINNs to more complex networks and incorporating dynamic and reactive processes to fully explore their capabilities.

The design, operations, and management of water distribution systems (WDS) involve complex mathematical models. These models are continually improving due to computational advancements, leading to better decision-making and more efficient WDS management. However, the significant time and effort required for modeling, programming, and analyzing results remain substantial challenges. Another issue is the professional burden, which confines the interaction with models, databases, and other sophisticated tools to a small group of experts, thereby causing non-technical stakeholders to depend on these experts or make decisions without modeling support. Furthermore, explaining model results is challenging even for experts, as it is often unclear which conditions cause the model to reach a certain state or recommend a specific policy. The recent advancements in large language models (LLM) open doors for a new stage in human-model interaction. This study proposes a framework of plain language interactions with hydraulic and water quality models based on LLM-EPANET architecture. This framework is tested with increasing levels of complexity of query to study the ability of LLMs to interact with WDS models, run complex simulations, and report simulation results. The performance of the proposed framework is evaluated across several categories of queries and hyperparameter configurations, demonstrating its potential to enhance decision-making processes in WDS management.

The implementation of real-time pressure management requires the acquisition of online measurement data, which is essential for the design of a closed-loop control system. In order to achieve this, it is necessary to determine in advance the locations for the installation of pressure sensors and pressure reducing valves (PRVs) for pressure reduction and control in a water distribution system (WDS). The sensors should be positioned at the most sensitive points to pressure variation, while the PRVs should be located at the points with the largest pressure drops. In this study, we solve this problem by extending an existing deterministic approach to a stochastic one with uncertain demand. For this purpose, we use the gap statistic to determine the number of sub-regions, the k-means++ to select the sensor positions, an edge-selection scheme for PRV localization, and differential evolution to minimize the total operating pressure. More importantly, we propose a scheme to calculate the sensitivity of the pressure and the pressure reduction due to the variation of the demand, based on which the formulated optimization is solved. Furthermore, the satisfaction of operational constraints, such as ensuring the pressure lower and upper bounds in the network, is formulated as chance constraints. Finally, a two-stage optimization framework is developed to solve this problem. In the lower level, Monte Carlo simulation is used to evaluate the impact of the uncertainties on the WDS performance. In the upper level, an optimizer determines the optimal locations for the pressure sensors and PRVs.

Combined sewer systems play a crucial role in sustaining urban environments by managing sewer and urban runoff flows. However, because of their combined nature, during wet weather events their capacity may be surpassed and untreated water may be discharged as combined sewer overflows (CSOs). Thus, optimizing the operation of combined sewer pump stations in real-time cannot only improve energy efficiency but also minimize pollution from CSOs. This task poses a multifaceted challenge due to the computational burden of generating an optimal policy in short run times, conflicting objectives, and several uncertainty factors regarding the inflow rates from rain runoff and domestic effluents. This study introduces a real-time decision-making framework that uses threshold triggers to set pump cut-in and cut-out levels as well as pump speed. Two objectives are targeted: minimizing total overflow volume and reducing energy consumption. The results demonstrate that incorporating dynamic pump speed adjustments alongside threshold-based triggers is an effective strategy for achieving these objectives, highlighting its potential for energy-efficient and environmentally responsible pump station operation.

Ensuring the safe quality of drinking water requires effective disinfection. Chlorine injection into water distribution systems is the most widely used method for this purpose. Scheduling the injection rates of chlorine from different available boosters poses a significant challenge, as the chlorine concentrations throughout various sections of the network must be maintained as close as possible to the desired levels and without violating lower and upper bounds. This task is highly complex due to the high dimensionality of water networks, the nonlinearity of hydraulics, and water quality dynamics. Furthermore, inherent uncertainties such as chlorine decay processes and variations in consumer demands exacerbate the complexity of decision-making. This study proposes a straightforward, yet practical approach based on sample average approximation (SAA) combined with robust optimization (RO). The proposed approach employs a linear model that minimizes the expected deviation of chlorine residuals from a target level across various demand scenarios, accounting for uncertainties in consumer demand. The uncertainty in the chlorine bulk reaction rate is addressed using robust optimization with a box uncertainty set. The method is tested on a benchmark water distribution system with different levels of uncertainty, and the results are compared with the deterministic case.

Drinking water post-treatment is far from sterile, and in situ microbial contamination occurs in the water distribution systems (WDS), leading to quality and health concerns. Computer-based tools, based on mechanistic models, that are adept at interpreting the microbial dynamics within distribution pipes, can be a practical approach to predict the microbiological quality fluctuations during WDS operation. Despite the availability of numerous such models, the high number of variables and parameters associated with them, and the complications in obtaining accurate high-resolution data, their calibration is quite difficult, and their real-world applications are challenging. This problem can be resolved by streamlining the mechanistic models via overseeing the processes within the WDS domain that are least sensitive to controlling the overall microbiological water quality. This would reduce the number of variables and the model parameters. Nevertheless, from our past analysis, we realized that such approaches might not guarantee many advantages in translating the existing mechanistic models to be user-friendly in terms of calibration. Therefore, in this direction, we propose an alternate approach involving applying data-driven modeling to develop surrogate models with minimal parameters to approximate the temporal dynamics of physicochemical and biochemical reactions within the aquatic domain. These surrogate models, once developed, can be integrated within the mechanistic modeling framework to arrive at streamlined models. Such models, with a significantly lesser number of variables and parameters than the existing ones, will be easy to calibrate and will facilitate the prediction of the spatiotemporal dynamics of microbiological quality in real-world WDS.

As part of the Battle of Water Networks competition series, the Battle of Water Demand Forecasting (BWDF) was organized in the context of the 3rd Water Distribution Systems Analysis and Computing and Control in the Water Industry (WDSA-CCWI) joint conference held in Ferrara (Italy) in 2024. In line with the previous editions of the Battle of Water Networks - the main objective of which was to address a specific problem related to the design and operation of water distribution networks - the BWDF aims to compare the effectiveness of methods for the short-term forecast of urban water demand in a set of real district metered areas. During the conference, 31 teams across the world participated in the BWDF and presented their approaches. The results obtained demonstrate the importance of (1) considering integrated approaches for short-term water demand forecasting; and (2) evaluating their performance in relation to more than one metric, case study, and period.

This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive CFD simulations. Tesla valves offer efficient passive fluid control without the need for external power, making them favorable in various applications. Previous observations indicated that Tesla valves effectively reduce the amplitude of pressure transients, prolonging their duration and distributing energy over an extended timeframe. While suggesting a potential role for Tesla valves as pressure dampers during transient events, the specific mechanisms behind this behavior remain unexplored. This research focuses on elucidating the internal dynamics of Tesla valves during transient events, aiming to unravel the processes responsible for the observed attenuation in pressure transients. This study reveals the emergence of “pressure pockets” within Tesla valves, deviating from conventional uniform pressure fronts. These pockets manifest as discrete chambers with varying lengths and volumes, contributing to the non-uniform propagation of pressure throughout the system. This investigation employs advanced CFD simulations as a crucial tool to unravel the governing dynamics of transient flow within Tesla valve configurations. By elucidating underlying fluid dynamics, this study lays the groundwork for future Tesla valve design optimization, holding potential implications for applications where the control of transient flow events is crucial.

Water distribution systems (WDS) and power grids (PG) are critical infrastructure systems that are vital to all human activity. As such, their quality of service is of great importance for economic, environmental, and human welfare reasons. Although traditionally being analyzed separately, the two systems are interconnected and can mutually affect one another. In order to utilize the potential benefits that the two systems can produce for each other, their design and operation should be analyzed conjunctively. In this paper, a conjunctive optimal design approach for water and power networks is presented, with the objective of finding the dimensions of the systems’ facilities that will result in minimal overall costs, for both design and operation. The model is formulated and implemented on two example applications using an off-the-shelf nonlinear solver by MATLAB and compared to the optimal design of the independent WDS. A sensitivity analysis is performed to provide validity to the obtained results. The conjunctive design is compared to the design of an independent WDS to emphasize the effect of including the PG in the optimization problem. Results show a clear link between the availability of renewable energy and sizing of WDS components. The design of the independent WDS leads to the violation of PG constraints, which are satisfied when including both systems under a single optimization model, demonstrating the importance of a holistic design approach.

Context and motivation: Climate change is manifested by climate variability, rising temperatures (and thus evaporation), and extreme events such as droughts and floods, which have a profound effect on the availability of natural resources, for example, high-quality water. While several technologies for addressing these challenges are available, their adoption is not widespread. In this study, a design thinking (DT) approach was applied to understand the problem space of floods and their handling by the Israeli water sector. Specifically, we aim at addressing the following question: What are the gaps in and barriers to adopting solutions that address sewerage flooding during extreme heavy rainfall events? The DT approach exposed major problems in the conduct of the water sector, including a lack of communication among organizations, the ill-defined distribution of responsibility, unclear and conflicting guidance, and insufficient funds and technological solutions, all hindering the possibility of adopting an integrative solution. This study demonstrates the role that DT plays in understanding a complex, multi-organizational problem space, in our case, the climate change readiness of the water sector, before delving into technological development. Any solution development should involve participants from the various organizations involved in the challenge. It is vital to address not only each organization’s requirements but also its technology adoption barriers and to initiate a comprehensive discussion, ultimately resulting in a shared understanding of all the facets of the challenge that can impact solution development and deployment.

Water distribution systems (WDSs) are designed to convey water from sources to consumers. Their operation is a main concern for engineers, researchers, and practitioners and is subject to demand, pressure, and quality constraints. Pumping stations require power to pump water and keep system pressure at a desired level. On the other hand, power is generally supplied through power grids (PGs), which require optimal operation while satisfying operational constraints, such as generation limits, power consumption, and voltage constraints. Since the two infrastructure systems are interconnected, decision-makers could benefit from a holistic approach that would allow solving the two operational optimization problems together as one conjunctive problem. This paper presents the full mathematical formulation of the conjunctive optimization problem, including a novel modelling approach for the operation of a variable speed pump, which does not include integer variables for pump status, thus allowing to solve the model as a non-linear programming (NLP) problem. The formulation is applied to two illustrative case studies, and the results are compared to the optimal operation of the independent WDS. The inclusion of the PG in the optimization problem is observed clearly in the results and influences them quite significantly. WDS operation is shown adjust to the PG constraints, and the application of the conjunctive model results in a cost reduction rate of more than 10 % for both case studies.

Water distribution systems (WDSs) are vital infrastructures designed to deliver water safely to consumers. This complex system necessitates continuous operational decisions, often optimized for efficiency. WDSs rely on power grids (PGs) to operate pumps and treatment facilities. PGs, likewise crucial, require strategic management to meet demand and environmental standards. Integrating the operation of both systems has garnered attention for its potential cost, energy, and environmental benefits. By leveraging the interconnection, trade-offs can be assessed, leading to improved solutions. Past research primarily focused on cost or carbon emissions as well as on hydraulic and voltage constraints. However, this study examines the impact of power systems on water quality, particularly water age, a key operational concern. Incorporating PGs into the optimal operation problem may alter flow directions, thereby influencing water age. Through mathematical modelling, this study evaluates these effects and applies them to simple case studies, demonstrating the influence of PG operation on water quality. Results demonstrate how tank constraints affect water age and show that a conjunctive operation approach, although beneficial for reduction of cost and energy consumption, can be damaging for water quality.

Prevention behaviors are important in mitigating the transmission of COVID-19. The protection motivation theory (PMT) links perceptions of risk and coping ability with the act of adopting prevention behaviors. The goal of this research is to test the application of the PMT in predicting adoption of prevention behaviors during the COVID-19 pandemic. Two research objectives are achieved to explore motivating factors for adopting prevention behaviors. (1) The first objective is to identify variables that are strong predictors of prevention behavior adoption. A data-driven approach is used to train Bayesian belief network (BBN) models using results of a survey of (Formula presented.) participants reporting risk perceptions and prevention behaviors during the COVID-19 pandemic. A large set of models are generated and analyzed to identify significant variables. (2) The second objective is to develop models based on the PMT to predict prevention behaviors. BBN models that predict prevention behaviors were developed using two approaches. In the first approach, a data-driven methodology trains models using survey data alone. In the second approach, expert knowledge is used to develop the structure of the BBN using PMT constructs. Results demonstrate that trust and experience with COVID-19 were important predictors for prevention measure adoption. Models that were developed using the PMT confirm relationships between coping appraisal, threat appraisal, and protective behaviors. Data-driven and PMT-based models perform similarly well, confirming the use of PMT in this context. Predicting adoption of social distancing behaviors provides insight for developing policies during pandemics.

Pressure reducing valves (PRVs) are essentially used to reduce operational pressures in water distribution systems (WDSs) to minimize water leakage. However, water age in a WDS is an important variable describing the water quality and should be kept as low as possible. Therefore, the aim of this study is to investigate the possibility and potential of simultaneously minimizing both pressure and water age by using PRVs. To determine the optimal location and setting of PRVs, a mixed-integer nonlinear programming (MINLP) problem is formulated with minimization of the sum of the weighted total water age and pressure as the objective function, where the weighting factor can be defined by the user’s preference. The equality constraints consist of the hydraulic equations and water age functions to describe pressure and water age in the distribution network, while the inequality constraints ensure them in the defined operating ranges, respectively. Applying the proposed approach to two case studies, the results show that both water age and pressure can indeed be significantly reduced by the optimized position and setting of the PRVs.

Hydrological modeling can be challenging due to significant data requirements and computational complexities. Hydrological models must be sufficiently complex to describe physical processes yet simple enough to use. This paper describes the development of a simplified watershed-scale input–output model to simulate runoff quantity and quality during a storm event. This work builds upon an existing semi-distributed rainfall–runoff model by adding calculations for pollutant concentrations based on simplified mass balance equations. The model was tested against various watershed examples of increasing complexity. The results show the change in peak flow and pollutant concentration in different areas of the watershed, demonstrating the model’s ability to account for the dynamics of runoff movement through the watershed. This paper advances watershed management by addressing data scarcity through the development of a simplified hydrological model that effectively incorporates spatial variability within a watershed while requiring minimal data input.

Historically, water utilities have relied on tried-and-true practices in the design and operation of their infrastructure, tapping new resources and expanding networks as needed. However, as the effects of climate change and/or urbanization increasingly impact both water supply and demand, utilities need new, holistic planning and management approaches. Integrated planning approaches must account for changing policies, technological progress, and unique, setting-specific operating conditions. Based on this notion, an international web seminar with faculty, researchers, and students from nine universities across five continents was conducted. In the 3-month seminar, participants were split into groups and tasked with developing future-proof, sustainable water management solutions for fictitious settings with unique resource availability, climate change predictions, demographic, and socioeconomic constraints. The goal of the seminar was to combine participants’ unique perspectives to tackle challenges in developing future water infrastructure, while forming lasting relationships. Water management concepts became more daring or “out-of-the-box” as the seminar progressed. Most groups opted for a holistic approach, optimizing existing infrastructure, integrating decentralized water management, furthering digitization, and fostering the adoption of innovative policy and planning strategies. To gauge their impact on the evolution of ideas, group dynamics and communication were observed throughout the seminar. As a result, the findings serve not only as a compendium of ideas and concepts for holistic design in the water sector, but also facilitate international collaboration, improve communication in cross-cultural teams or guide the development of training programs in water management for researchers, professional engineers, or water utilities.

Water distribution systems (WDSs) are massive infrastructure systems designed to supply water from sources to consumers. The optimal operation problem of WDSs is the problem of determining pump and tank operation to meet the consumers’ demands with minimal operating cost, under different constraints, which often include hydraulic feasibility, pressure boundaries, and water quality standards. The water quality aspect of WDSs’ operation poses significant challenges due to its complex mathematical nature. Determined by mixing in the systems’ nodes, it is affected by flow directions, which are subject to change based on the hydraulic state of the system and are therefore difficult to either predict, control, or be included in an analytical model used for optimization. Water age, which is defined as the time water travels in the system until reaching the consumer, is often used as a general water quality indicator—high values of water age imply low water quality, whereas low values of water age usually mean fresher, cleaner, and safer water. In this work, we present the effects that tank operation has on water age. As tanks contain large amounts of water for long periods of time, water tends to age there significantly, which translates into older water being supplied to consumers. By constraining the tank operation, we aim to present the trade-off between water age, tank operation, and operational cost in the WDS optimal operation problem and provide an operational tool that could assist system operators to decide how to operate their system, based on their budget and desired water age boundary. The analysis is applied to three case studies that vary in size and complexity, using MATLAB version R2021b and EPANET 2.2. The presented results show an ability to mitigate high water age in water networks through tank constraints, which varies in accordance with the system’s complexity and tank dominance in supply. The importance of a visual tool that serves as a guide for operators to tackle the complex problem of controlling water age is demonstrated as well.

This study examined the utilization of transient shear stress for cleaning sediment and biofilm materials adhered to the inner walls of pipes in water distribution systems. This approach is presented as a potential method for material resuspension during pipe flushing. This is done to prevent discoloration and low-water-quality events, which can be a major concern for both water utilities and consumers. This work builds and expands on previous work on biofilm detachment under unsteady flows. This study explored the manipulation of existing valves to induce high transient shear stresses that, in turn, resuspend materials adhering to the pipe walls. Through the use of valves strategically positioned along the water distribution system, the systems are subjected to consecutive controlled transient pressure waves. It is possible to capitalize on the interference properties of the controlled transient waves at various points in the system. It is in this condition that various pressure waves can merge within the system, causing high pressures and relatively high shear stresses. However, it is vital to keep the pressure confined within the allowed range to ensure the integrity of the system. A Lagrangian with unsteady friction method was implemented and applied for modeling the transient behavior in two water networks.

The COVID-19 pandemic changed daily routines for people around the globe due to the adoption of social distancing measures, such as working from home and restricted travel. Changes in daily routines created new water demand patterns, and the spatial redistribution of water demands in urban water distribution systems affected water quality. A range of factors can influence individual decisions to social distance, including demographics, risk perceptions, and prior experience with infectious disease. This research develops an agent-based modeling framework to simulate decisions to social distance, the effect of social distancing on water demands, and effects on the performance of water infrastructure and the quality of delivered drinking water. This framework couples a hydraulic model, a COVID-19 transmission model, and Bayesian belief network (BBN) driven decision-making models within an agent-based modeling framework. The model is applied for a virtual city, Micropolis, to explore the effects of social distancing decisions on water age. Results demonstrate an increase in average water age and changes to the expected flow directions in pipes under scenarios of increasing social distancing. Nodes near industrial areas experience higher degradation of water quality. This research provides a new framework to develop and evaluate water infrastructure management strategies during pandemics.

Applying mechanistic water quality analysis models that are proficient at simulating the dynamics of heterotrophic bacteria within distribution pipes is a pragmatic approach to maintaining biological stability during drinking water distribution systems (DWDS) operation. Accurate interpretation of hydrodynamics and the uncertainties associated with the multifaceted exchanges within the distribution pipes is crucial to the reliability of these models' predictions. However, knowledge about most exchanges within DWDS is still inadequate. Therefore, state-of-the-art mechanistic models exist merely as theoretical frameworks to understand the causes and effects of microbiological quality fluctuations in DWDS, and they lack general applicability. Advancing the applicability and reliability of the mechanistic models necessitates adequate consideration of epistemic and aleatory uncertainties. This study developed mechanistic models to realize the degree of complexity that needs to be integrated into the modeling framework to accurately describe the water quality dynamics in a real-world DWDS. Under the test conditions considered, the simplest single-phase models that ignore the complex exchanges associated with the pipe biofilm layers were found to make similar microbiological quality predictions as the relatively complicated two-phase models. The results indicate that the knowledge uncertainty associated with mechanisms concerning heterotrophic bacterial regrowth in the bulk phase and biofilm detachment in the wall phase is critical in controlling the reliability of the mechanistic water quality models.

The average time water takes from the source to the consumer is referred to as the "water age." It is a general indicator of water quality as it affects the disinfectant levels, microbial regrowth, and the contaminants' buildup. So, water suppliers strive to balance water age and quality to provide safe and reliable drinking water. Hydraulic modeling is the most straightforward way to determine water age. Several hydraulic simulation packages are available in the public and commercial domains for calculating the water age. In recent years, several researchers have used electrical simulators for water distribution network (WDN) analysis. The use of electrical simulators for water quality analysis has not yet been reported in the literature. This study proposes a methodological approach for calculating the water age for WDN in an electrical simulator. In this method, the initial water ages in reservoirs are replaced with voltage sources, nodal demands with current sources, and pipes with a new element called a "pipe delay." The value of pipe delay depends on the pipe parameters (e.g., length and diameter) and flow through the pipe. The applicability of the freely downloadable electrical simulator, QucsStudio, to calculate water age through Verilog-A code is demonstrated for several pipe networks. The results obtained from the hydraulic simulator, EPANET, are taken as benchmarks to verify the accuracy of the proposed methodology. The electrical simulator is shown to provide highly accurate results regarding water age at junctions.

As the population increases and covers more land, water distribution systems (WDSs) also expand to deliver potable water at adequate pressure, an essential service to communities around the world. The hydraulic networks that represent WDSs are large and complex dynamic systems. Hence, appropriate modeling methods are needed to identify influential zones or areas where water utilities can implement comprehensive measurement programs and infer the results to the rest of the network. This work presents an effective clustering method to partition water distribution systems into sub-networks and identify influential areas that can be used as hot spots by water utilities to retrofit their systems with advanced metering infrastructure components. An adapted K-means clustering algorithm analysis is applied to two benchmark hydraulic networks to highlight the effects of the different parameters of interest for clustering, that is, pressure and demand, different weights used as edge distances, and network topologies. An exhaustive search method algorithm is implemented to minimize the variation of the parameter of interest among clusters. The results show influential areas at the sub-network level represented by the clusters' centroids that can be used to infer the hydraulic conditions of other areas with different levels of accuracy. Minimum demand variation is achieved when using a combination of hydraulic and topological characteristics as edge weights. The clustering models will help researchers and practitioners select an effective partitioning tool to improve the management of water distribution systems.

Water distribution systems (WDS) and power grids (PG) are critical infrastructure systems that are vital to all human activity. As such, their quality of service is of great importance for economic, environmental, and human welfare reasons. Although traditionally the two systems are analyzed separately, they are interconnected and have mutual effects on one another. WDSs are some of the largest energy consumers, with 7%-8% of the world's total generated energy used for drinking water production and distribution. At the same time, WDS storage facilities allow regulating power loads by load shifting operation policies and even storing energy by using turbines. Therefore, decisions made as part of operating one system influence the operation policy of the other. In order to utilize the potential benefits that the two systems can produce for each other, their design and operation should be analyzed conjunctively. In this paper, a conjunctive optimal design approach for water and power networks is presented, aimed at finding the dimensions of the systems' facilities that will result in minimal overall costs, for both design and operation. The model is formulated, implemented on a simple case study using a nonlinear solver through MATLAB, and analyzed using a comparison to the optimal design of the independent WDS.

This paper presents a new approach to the integrated optimal operation of water distribution systems (WDS) and power distribution systems (PDS) under uncertain conditions. Recognizing the intricate interdependencies between these systems, the paper proposes an optimization model grounded in adjustable robust optimization (ARO). The model concurrently addresses optimal power dispatch and pump scheduling under various uncertainties, including fluctuating power loads and water demands, and renewable power availability. The ARO framework stands out for its real-time adaptability to uncertainty realizations, providing a tractable and effective solution for complex system management. It is validated through a comprehensive case study, demonstrating its applicability and effectiveness. The results provide compelling evidence of the benefits of integrated system operation. While uncertainties in PDS have a pronounced impact on operational cost, the WDS demand uncertainty can be effectively managed through strategic adjustments in pumping schedules. Notably, the cost implications of these adjustments in the face of WDS uncertainties are moderated and only affect the objective minorly.

Urban water distribution systems (WDSs) are designed to deliver potable water to all end users. Unpredicted changes in water demands and hydraulics can increase residence time in pipes (water age), leading to growth of microbes and decreased water quality at some locations in a network. In response to reduced water quality, consumers may reduce demands for drinking, cooking, and cleaning. Lack of access to clean water can create high costs for some households due to the cost of using bottled water, paper plates, and laundromats for daily activities. This research develops an optimization framework to design operational strategies that maximize equity in a community that uses a WDS. Reduced demands and inequitable access to clean water are explored in this research in the context of the COVID-19 pandemic through a coupled framework. First, an agent-based modeling (ABM) framework is applied to simulate COVID-19 transmission, social distancing decision-making, and reductions in water demands. Large-scale reductions in demands, especially in industrial and commercial areas as individuals worked from home, leads to hot-spots of increased water age. The ABM is extended in this work to simulate households that choose to reduce demand from the system by buying bottled water for cooking, cleaning, and hygienic purposes. Equity is evaluated using an adjusted income metric that includes the cost of water bills and supplemental bottled water. A graph theory approach is applied to open and close valves to maximize equity. The coupled framework is applied for a virtual water distribution system, and results demonstrate operational strategies that improve equity for a community. This research develops an equity metric that assesses the water quality of delivered water and can be used to facilitate WDS management that provides equitable access to clean water.

Water distribution systems (WDS) and power grids are critical infrastructure systems ensuring everyday human activity. A significant amount of research effort has been dedicated to finding optimal operation policies for each of those systems. WDS power consumption creates a dependency between the operation of those two systems, and several recent studies have dealt with the optimization of their conjunctive operation, also known as optimal water and power flow problem (OWPF). The combination of the WDS optimal operation problem and the optimal power flow (OPF) problem results in a non-convex MINLP problem, which poses significant mathematical and computational challenges. Previous studies have used different types of approximation methods to convexify the problem and obtain feasible solutions. These methods often lead to local optima and do not provide theoretical guarantees of global optimality. This study presents a tailored solution method for optimizing the conjunctive operation of WDS and power grids. The method relies on implementing polyhedral relaxations of the non-convex hydraulic constraints, together with conic relaxations to overcome non-linearities in the OPF problem. The approach allows for fast convergence and reduces running time significantly, which allows the solution of large-scale OWPF problems. Furthermore, the use of convex relaxations provides optimality gaps for the computed solutions. The method is tested on an example application, and its performance is compared with that of an off-the-shelf non-linear solver.

In the realm of practical water distribution systems (WDS), uncertainties in hydraulic and water quality modeling affect the management of WDS making it a multifaceted challenge. Furthermore, the incorporation of water quality considerations into WDS design and management has become paramount, necessitating the use of precise water quality models. The conventional water quality models employed in drinking WDS have faced inaccuracy due to their underlying assumption of instantaneous and complete mixing at junctions. To address this limitation, recent models such as EPANET-IMX (Incomplete Mixing Extension), EPANET-BAM, and AZRED have emerged, incorporating empirical equations to model the nuances of incomplete mixing. These advancements offer improved accuracy for water quality analysis within WDS. However, the presence of uncertainty in disinfectant reaction rates also presents an obstacle to achieving optimal water quality management. Within such systems, determining the scheduling of booster disinfectant dosages is a challenge. In response, this study seeks to determine the optimal dosage schedule for booster disinfectants while accounting for fluctuations in bulk reaction rate coefficients and acknowledging incomplete mixing at junctions. To tackle this uncertain optimization problem, robust optimization principles are employed. The study applies these principles to a small-scale network as an illustrative example, showcasing robust optimal schedules. Two water quality models, namely EPANET and EPANET-IMX, are utilized for water quality simulations. The resulting optimal schedules are compared and analyzed across all three models. It was observed that considering the incomplete mixing varied the optimal booster dosage by about 10%. The findings emphasized the importance of considering incomplete mixing in both water quality analysis and optimization endeavors.

Rapidly growing cities need significant extensions to their water distribution networks to fulfil the water demands of the population. The design of such systems is still challenging to optimise between the robustness/reliability/vulnerability and the cost. This study presents an idea: If the network layout is already determined, how can optimal diameters be found that balance cost and service quality? On the one hand, the purpose is to determine the cheapest possible network that can still serve every consumer. On the other hand, we extend the idea by optimising all backups of the network.

This research explores the integrated management of water distribution systems (WDS) and power distribution systems (PDS) to improve their resilience to extreme scenarios. This study delves into the dynamics of a locally managed PDS as an example of extreme operational conditions. The primary objective is to minimize load shedding (LS) in the PDS through strategic load shifting in the interconnected WDS, demonstrating the potential of cooperative decision making between the two critical systems. The optimization framework offers a novel approach to managing flexible resources during emergencies by utilizing the mutual links between a WDS and a PDS. Typically, WDSs and PDSs are operated by different operators such that cooperation is limited. This study presents how communication based on limited information sharing between the two systems is sufficient to increase resilience and improve the systems’ functionality, emphasizing the advantages of cooperative decision making. This paper highlights the significance of cross-sectoral collaboration, presenting a viable pathway for managing local infrastructure systems under extreme conditions while ensuring uninterrupted service delivery to communities.

Recent developments in control theory coupled with the growing availability of real-time data have paved the way for improved data-driven control methodologies. This study explores the application of a data-enabled predictive control (DeePC) algorithm to optimize the operation of water distribution systems (WDS). WDSs are characterized by inherent uncertainties and complex nonlinear dynamics. Hence, classic control strategies that involve physical model-based methods are often hard to implement and infeasible to scale. The DeePC method suggests a paradigm shift by utilizing a data-driven approach. This method employs real-time data to dynamically learn an unknown system’s behavior. It utilizes a finite set of input–output samples (control settings, and measured data) to derive optimal policies, effectively bypassing the need for an explicit mathematical model of the system. In this study, DeePC is applied to a pressure management case study and demonstrates superior performance compared to standard control strategies.

Most existing approaches to ensuring water quality in water distribution systems (WDSs) are deterministic, i.e., they do not consider uncertainties, although they may have significant impacts on the water quality. It is well recognized that water demand represents a predominant uncertainty in a WDS. In addition, water age is often used as an important parameter to describe the water quality in a WDS and can be influenced by water demand and control elements such as pressure-reducing valves (PRVs). Therefore, the aim of this study is to carry out a probabilistic analysis of the impact of demand uncertainty on the water age in the distribution network. Based on the solution of deterministic optimization to minimize the water age, Monte Carlo simulation will be carried out by sampling the uncertain demand to evaluate the stochastic distribution of water age, as well as other operating variables like pressure and flow. As a result, the probability of violating the constraints of such variables can be determined, with the reliability of the operating strategy (e.g., the settings of the PRVs) given by deterministic optimization provided. In cases of low reliability, it is necessary to modify the operating strategy in order to decrease the probability of constraint violation. For this purpose, a chance-constrained optimization problem is formulated, and its benefits for ensuring the user-defined reliability are studied. A benchmark network is used to verify the proposed approach.

This study focuses on optimizing time series forecasting models for water demand in a North Italian city as part of the Battle of the Water Demand Forecast (BWDF) challenge. It aims to accurately predict water demands across ten district-metered areas (DMAs) using historical data and weather information over a one-week horizon. The methodology encompasses data preprocessing, including missing data imputation, feature engineering, and novel normalization techniques, followed by the development and hyperparameter optimization of various data-driven models such as random forest, XGB, LSTM, and Prophet. Extensive cross-validation tests assess each model’s performance, revealing that our refined approach markedly enhances forecast accuracy, demonstrating the importance of model and parameter selection for effective water demand forecasting.

High-quality drinking water is an essential need of every modern settlement. Typical analysis applies the EPANET to calculate the water age and the chlorine distribution. However, it cannot cope with diffusion or three-dimensional effects. This study aims to find the potential cases where the traditional modelling needs adjustment or improvements. This study uses computational fluid dynamics to analyse how non-reacting contaminants (e.g., fluoride, chloride, metal oxides, and micropollutants) spread in water distribution networks.

Water distribution systems (WDSs) are designed to deliver potable water across urban areas. Unpredicted changes in water demands and hydraulics can increase the residence time in pipes, leading to the growth of microbes and decreased water quality at some locations in a network. During the COVID-19 pandemic, large-scale reductions in demands, especially in industrial and commercial areas as individuals worked from home, led to hot-spots of increased water age. In response to reduced water quality, consumers may avoid using tap water for end uses including drinking, cooking, and cleaning. The lack of access to clean water can create high costs for some households due to the cost of buying bottled water. Inequitable access to safe, affordable water is explored in this research in the context of the COVID-19 pandemic through a coupled framework. This research extends an existing agent-based modeling (ABM) framework that simulated COVID-19 transmission, social distancing decision-making, reductions in water demands, and flows in a water distribution system. The ABM is extended in this work to simulate households that perceive water quality problems with tap water and choose to buy bottled water for cooking, cleaning, and hygienic purposes. Agents choose tap water avoidance behaviors based on water age, a surrogate for water quality. Equity is evaluated using the cost of water, both tap and bottled, as a percentage of income. An optimization approach is coupled with the ABM framework and applied to design operational strategies that improve equitable access to safe affordable water. A graph theory approach identifies valves that should be opened and closed to improve water quality at nodes and maximize equity. The results demonstrate an increase in water age due to social distancing behaviors, and water of high age is observed to be disproportionately located near industrial areas. Adjusted income demonstrates inequities in access to safe and affordable water. Operational strategies are developed to improve equity for a community through valve operations that improve the equitable delivery of safe water. This research develops an approach to assess equity of the quality of delivered water and can be used to facilitate WDS management that provides equitable access to safe water.

This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive computational fluid dynamics (CFD) simulations. Previous observations indicated that Tesla valves effectively reduce the amplitude of pressure transients, prolonging their duration and distributing energy over an extended timeframe. While suggesting a potential role for Tesla valves as pressure dampers during transient events, the specific mechanisms behind this behavior remain unexplored. The research focuses on elucidating the internal dynamics of Tesla valves during transient events, aiming to unravel the processes responsible for the observed attenuation in pressure transients. The study reveals the emergence of distinctive “pressure pockets” within Tesla valves, deviating from conventional uniform pressure fronts. These pockets manifest as discrete chambers with varying lengths and volumes, contributing to a non-uniform propagation of pressure throughout the system.

Water distribution systems (WDSs) are critical infrastructure systems designed to safely supply water to consumers. As complex systems, they require constant operational decision-making, which is often the result of an optimization process. WDSs require power for pumping and the operation of water treatment facilities. Power is supplied through power grids (PGs)—essential infrastructure which must be strategically operated as well, under constraints. This work is focused on the effects of PG operation on water quality, which is a major operational challenge of WDSs. The inclusion of the PG as part of the WDS optimal operation problem has the potential of influencing flow directions in the WDS, which in turn affects water quality. In this work, a model for the optimal operation of water and power networks is constructed, including water age considerations. The model is applied to a simple case study, containing a small WDS connected to a small PG. The results demonstrate the effect of PG operation on water quality.

Modelling and assessing water quality parameters in water distribution networks is essential for providing safe drinking water to end users. While simulation-based modeling approaches rely on costly differentiation for numerical solvers, surrogate models using Artificial Neural Networks (ANNs) can predict solutions with minimal computational effort. In this work, we formulate the idea of a universal surrogate model for predicting water quality dynamics that, once trained, will apply to all water distribution networks. To this end, we adapt the idea of meta-parameterized ANNs to account for variable boundary and initial conditions.

Intermittent water supply (IWS) systems, originally intended for continuous supply, have been compelled to adopt intermittent supply due to factors such as water scarcity, financial limitations, ineffective operational tactics, unexpected increases in demand, and infrastructure deterioration. In response, consumers have adapted by employing flexible behaviors and utilizing storage tanks to manage water during non-supply periods. This study aims to present a methodology for devising an optimal schedule for intermittent operations, prioritizing consumer equity. The framework is tailored to a real-world intermittent network in rural South India, accounting for practical constraints and fluctuations in demand. This article only shows the preliminary analysis of the system; the development of the optimization framework is still a work in progress.

The optimal operation of water distribution systems (WDS) is a paramount objective for water utilities due to the substantial energy consumption associated with pumping. A major challenge in optimizing WDS operation is addressing uncertainties such as those related to consumer demands. Real-time operation under uncertainty necessitates a dynamic approach that can utilize the newly observed information and adjust the operational policy accordingly. This study presents an adjustable robust optimization (ARO) approach to tackle this challenge. Unlike static optimization methods, ARO generates a decision rule policy that is dynamically adjusted as new data becomes available and the operational horizon evolves, thereby ensuring adaptability to changing conditions. Furthermore, the study includes a quantitative analysis of typical demand uncertainty that supports the formulation of the ARO model. The proposed method is evaluated through two case studies and compared with traditional folding horizon approaches. The results indicate that the ARO method is competitive with traditional methods in terms of objective value and surpasses them in terms of robustness. An additional advantage of the method is its offline operation capability which enables it to produce decision rules independent of real-time programs. This feature facilitates various practical applications such as what-if analyses, maintenance work planning, and preparation for other special events.

Maximizing reliability is imperative for water distribution network (WDN) design. Recent claims about providing adequate and operable isolation valves as a focal aspect of a reliable WDN design have attracted substantial interest. In this direction, this paper attempts to answer the question of which isolation valves in a WDN must be prioritized based on their criticality toward enhancing WDN reliability. Two novel algorithms are proposed, one for ranking every isolation valve in a WDN and the second to identify the location of a new isolation valve to reduce the criticality of a specific valve in a WDN. Unlike the prevailing methodologies, the proposed algorithms are entirely based on the topological attributes of a WDN and do not require a calibrated hydraulic model, which is often a limiting factor in WDN management. Our proof of concept has unveiled the capability of the methodology to deliver preliminary information to water utilities regarding the criticality of valves without any hydraulic simulation.

A substantial number of water distribution systems (WDS) worldwide are operated as intermittent water supply (IWS) systems, delivering water to consumers in irregular and unreliable manners. The IWS consumers commonly adapt to flexible consumption behaviors characterized by storing the limited water available during shorter supply periods in intermediate storage facilities for subsequent usage during more extended nonsupply periods. Nevertheless, the impacts of such consumer behavior on the performance of IWS systems have not been adequately addressed. Toward this direction, this article presents a novel open-source Python-based simulation tool (EPyT-IWS) for WDS, virtually acting like an IWS modeling extension of EPANET 2.2. The applicability of EPyT-IWS was demonstrated by conducting hydraulic simulations of a typical WDS with representative IWS attributes. Different IWS operation cases were considered by varying the amount and consistency of the water availability to the consumers. EPyT-IWS outputs showed that domestic storage of water within underground tanks and subsequent pumping into overhead tanks allows consumers to cope with the intermittent water availability and suitably meet their demands. Besides the interval, the clock time of the water supply was predicted to influence IWS consumers’ ability to meet water demands.

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Water distribution network design is a complex multi-objective optimization problem and multi-objective evolutionary algorithms (MOEAs) such as NSGA II have been widely used to solve this optimization problem. However, as networks get larger, NSGA II struggles to find the diverse and uniform solutions that are critical in multi-objective optimization. This research proposes an improved version of NSGA II that uses three new-generation methods to target different regions of the Pareto front and thus increase the number of solutions in critical regions. These methods include saving an archive, local search around extreme and uncrowded Pareto front, and local search around the knee area of the Pareto front. The improved NSGA II is tested on benchmark networks of different sizes and compared to the best-known Pareto front of the networks determined by MOEAs. The results show that the proposed algorithm outperforms the original NSGA II in terms of broadening the Pareto front solution range, increasing solution density, and discovering more non-dominated solutions. The improved NSGA II can find solutions that cover all parts of the Pareto front using a single algorithm without increasing computational effort.

Water distribution systems (WDSs) require high-quality water for safe consumption. To achieve this, disinfectants such as chlorine are often added to the water in the system. However, it is important to regulate the levels of chlorine to ensure they fall within acceptable limits. The higher limit is to control disinfection by-products, while the lower limit is established to guarantee that the water is free of organic contaminants. The rate at which chlorine reacts within the pipes is affected by various factors, such as the type of pipe, its age, the pH level of the water, the temperature, and others. This variability makes it challenging to accurately model water quality in WDSs, which can impact the optimal rate of booster injection. To address the uncertainty in the chlorine reaction rate, the current research proposes a robust counterpart reformulation of the booster chlorination scheduling problem, which considers the chlorination reaction rate as uncertain. The proposed reformulation was tested on two benchmark WDSs and analyzed with a thorough sensitivity analysis. The results showed that as the size of the uncertainty set increased, the injection mass also increased. This reformulated approach can be applied to any WDS and provides a way to obtain optimal scheduling within the desired protection levels.

Rainwater harvesting is an ancient practice currently used for flood and drought risk mitigation. It is a well-known solution with different levels of advanced technology associated with it. This study is aimed at reviewing the state of the art with regards to rainwater harvesting, treatment, and management. It focuses on the environmental and social benefits of rainwater harvesting and links them to the Sustainable Development Goals. The review identifies characteristics of laws and regulations that encourage this practice and their current limitations. It presents methodologies to design a rainwater harvesting system, describes the influence of design variables, and the impact of temporal and spatial scales on the system’s performance. The manuscript also analyzes the most advanced technologies for rainwater treatment, providing insights into various processes by discussing diverse physiochemical and biological technology options that are in the early stages of development. Finally, it introduces trends and perspectives which serve to increase rainwater harvesting, water reuse, and effective management.

The operation of water distribution systems (WDS) is an energy-intensive process, which is subject to constraints such as consumer demands, water quality, and pressure domains. As such, tracing an operation policy in which constraints are met while energy costs are minimized, is a foremost objective for water utilities. Given the inherent uncertainties in WDS operation and the importance of supply continuity, it is essential to find an operational strategy that is robust against a wide range of circumstances. One promising approach for optimization under uncertainty is robust optimization (RO), which assures a robust (feasible) solution to realizations of the uncertain parameters, within predefined bounds. This study presents an RO-based method for optimizing pump scheduling under uncertainties of consumer demands and pumping costs. The method can capture various types of correlations between the uncertain parameters, thus better reflecting the uncertain nature of WDS operation. The developed methodology is demonstrated in two case studies with different levels of complexity. The impacts of uncertainty levels and correlation coefficients are analyzed to demonstrate their implications on operation policy. The results show the advantages of using RO with tradeoffs between costs and constraints satisfaction.

Water age is the time taking the water to travel through a distribution system and reach the consumer. Generally, there is a trade-off between water pressure and water age in a water distribution system - higher pressure results in higher flow velocity, which often means shorter travelling time for the water, while lower pressure leads to slower flow and thus higher water age. Low pressure is a desired objective in a distribution system, as it reduces the physical stress on its components and minimizes water losses in an event of a leak. Low water age is a desired objective as well, as increased age is regarded as low water quality. Therefore, the two objectives compete with one another. The problem of trying to minimize both water pressure and age is a common problem in water distribution system design and management. This paper introduces an algorithm for pressure reducing valve (PRV) placement for reducing water age in water distribution systems. The algorithm is based on graph-theory elements and uses EPANET 2.2 for simulation and analysis. The method is demonstrated on a small-scale example, and the results present relatively significant improvements in respect to water age.

Water distribution networks (WDNs) are a vital component of urban water infrastructure. They transport water from production sites (sources) to spatially distributed consumers (sinks). Multiobjective optimization procedures are often used to minimize construction costs and at the same time maximize the resilience of such systems, which is usually a very computationally expensive task. Recently, highly efficient approaches based on complex network analysis (CNA) have been developed to solve this task more computationally efficiently. With CNA, very large WDNs can be optimized, considering network topology and demand distribution (using, e.g., demand edge betweenness centrality). However, existing CNA approaches do not consider network topography (i.e., height differences between sources and sinks). Comparing design solutions based on CNA with those found by evolutionary algorithms shows that the least-cost CNA design cannot compete with the latter. In this work, a hybrid approach is developed, where low-cost design CNA solutions are evaluated with a hydraulic solver (Epanet2), and subsequently the demand edge betweenness centrality distribution is iteratively altered for nodes with pressure deficits. This enhanced CNA-based optimization is tested on two different large case studies from the literature and shows promising results (2% cost increase). These solutions were obtained using significantly less computational effort (at least factor 1,000 faster), enabling solving very large WDN optimization problems (>150,000 decision variables).

Ensuring consistent and high-level water quality is paramount for water utilities to meet health requirements and attain customer satisfaction. To this end, water utilities need to constantly surveil all relevant water quality parameters, for example, chlorine-concentration, as well as to optimally control dosage rates in their drinking water distribution systems (DWDSs). Simulation models coupling DWDS hydraulics and water quality have been well established and highly accurate. However, they are computationally very expensive such that optimization of control parameters may only be possible to a very limited extent. In this work, we are proposing the use of a lightweight, machine learning-based surrogate model for the coupled simulation of hydraulic and water quality parameters that may serve to reduce simulation times and render optimization of control parameters more efficient. The baseline model is system-specific and learns to predict the steady-state hydraulic and water quality state simultaneously based on common inputs to a DWDS model, that is, water demands and dosage rates at reservoir levels. Results indicate good prediction capabilities of the surrogate model with R²values greater than 0.98 as well as error rates below 0.01% for hydraulic parameters and below 1% for water quality parameters. Some slight spatial trends in the prediction error's variance are identified for hydraulics as well as for water quality parameters.

Power and water networks are massive critical infrastructure systems that are strongly coupled through the power transmitted from the power network to the water network, yet they are commonly operated independently. The resemblance between the two systems is high: a power network comprises a system of power stations, power transmission, and power distribution to individual customers, while a water network likely consists of water sources, regional pipelines, and a distribution system for supplying its end users. The physics of both systems are governed by Kirchhoff's laws of continuity of flow/current at nodes and energy pressure/voltage along paths. Smart joint operation of water and power networks has the potential of saving water, saving energy, and reducing environmental impact. Several studies have dealt with the problem. However, modeling can still be significantly improved, and many different research directions can be explored. The complexity of managing both systems independently differs according to the adopted assumptions and can be as easy as solving a linear programming (LP) problem or can end up as a challenging large-scale mixed integer nonlinear problem (MINLP). The objective of this study is to formulate and solve a conjunctive water-power flow (OWPF) problem for optimizing both systems simultaneously. The formulation and solution scheme will be demonstrated on a small yet general layout of a coupled power-water network.

Water distribution systems (WDSs) deliver clean, safe drinking water to consumers, providing an essential service to constituents. WDSs are increasingly at risk of contamination due to aging infrastructure and intentional acts that are possible through cyber-physical vulnerabilities. Identifying the source of a contamination event is challenging due to limited system-wide water quality monitoring and non-uniqueness present in solving inverse problems to identify source characteristics. In addition, changes in the expected demand patterns that are caused by, for example, social distancing during a pandemic, adoption of water conservation behaviors, or use of decentralized water sources can change the anticipated propagation of contaminant plumes in a network. This research develops a computational framework to characterize contamination sources using machine learning (ML) techniques and simulate water demands and human exposure to a contaminant using agent-based modeling (ABM). An ABM framework is developed to simulate demand changes during the COVID-19 pandemic. The ABM simulates population movement dynamics, transmission of COVID-19 within a community, decisions to social distance, and changes in demands that occur due to social distancing decisions. The ABM is coupled with a hydraulic simulation model, which calculates flows in the network to simulate the movement of a contaminant plume in the network for several contamination event scenarios. ML algorithms are applied to determine the location of source nodes. Research results demonstrate that ML using random forests can identify source nodes based on inline and mobile sensor data. Sensitivity analysis is conducted to explore the number of mobile sensors that are needed to accurately identify the source node. Rapidly identifying contamination source nodes can increase the speed of response to a contamination event, reducing the impact to the community and increasing the resiliency of WDSs during periods of changing demands.

Water distribution systems (WDS) are critical to delivering clean, healthy water at the required quantities and qualities. They are exposed to different kinds of attacks, both physical and cyber, as well as accidental contamination events. In the past years, several events occurred in which contaminants polluted the drinking water. Most of the cases were accidental, but there had also been criminal acts involved. Placing water quality monitoring sensors in designated locations throughout the WDS is considered one of the most effective protection methods for coping with deliberate or occasional water quality contamination intrusions. Sensors, which comprise the water quality monitoring system, communicate over a cyberinfrastructure layer (via radio, cellular, satellite, or wire) and, as such, are exposed to cyber-attacks. Traditionally, sensor placement methodologies included objective functions such as the polluted volume of water consumed or the time for the first detection of possible pollution. However, so far, water quality sensor placement algorithms and tools have not considered cases where the sensor network itself is attacked or malfunctions in a way that part of its components is deactivated. In this study, we propose to develop and demonstrate a methodology for evaluating the impact of a cyber-attack on a sensor network compromising part of its functionality. Our proof-of-concept, using a simple network and straightforward cyber-attack scenario, has revealed potential in analyzing the performance of sensor networks under attack and can provide valuable information for decision-makers in the water utilities and regulators.

Post-treatment drinking water is far from sterile, so microbial regrowth occurs, and the distribution pipes behave like a dynamic ecological niche for microbes. Unwarranted microbial regrowth within distribution pipes can lead to quality issues and health concerns. The application of computer-based tools adept at mechanistically simulating the dynamics of heterotrophic bacteria within distribution pipes is a practical approach to safeguarding biological stability during drinking water distribution systems (DWDS) operation. The imperfect understanding of most of the stochastic processes related to the heterotrophic bacterial dynamics and the natural randomness of the system constraints make mechanistic modeling complicated. Within the context of the current state of the art, the questions on the level of detailing in describing the processes within the pipe domain for mechanistically describing the microbiological quality with a certain acceptable level of accuracy are still unanswered. In this regard, we attempt to answer these questions and overcome the limitations of the state of the art to develop a practically relevant modeling tool for DWDS management. The specific objectives of this study are to (1) develop stochastic mechanistic models considering the three-level uncertainties - model parameters (kinetic rate constants), model coefficients (dispersion coefficient), and state variables (source quality characteristics); (2) understand the level of complexity that needs to be integrated into modeling to explain the water quality dynamics correctly; and (3) present the applicability of the developed models for simulating microbiological quality fluctuations in a real-world DWDS.

To ensure high-quality water in water distribution systems (WDS), disinfectant, generally chlorine, is injected into the system. However, this chlorine limits should be kept within acceptable levels. The higher limit is enforced to control the disinfection by-products, whereas lower limit is enforced to ensure that the water is organic contaminant free. The chlorine reaction rate within the pipe systems varies depending on the type of pipe, age, water PH level, temperature, and many other factors. This hinders the accurate water quality modelling in water distribution systems which in turn affects the optimal amount of booster injection rate. To minimize the risk of the uncertainty in chlorine reaction rate, current work suggests a robust counterpart reformulation of the optimal booster chlorination scheduling problem considering the rate of reaction of chlorination as uncertain. The proposed reformulation is tested on a benchmark WDS, and the results are compared with deterministic case. The results indicated that with an increase in the protection level the injection mass increased and with very large protection levels, the formulation resulted in no solution. The proposed reformulation of the traditional approach can be applied to any WDS and obtain optimal scheduling with appropriate protection levels.

The goal of water distribution systems (WDSs) is to supply water to consumers. Pumping and distributing water is an energy-intensive process, and it is also subject to different constraints such as demand satisfaction, water quality, pressure boundaries, etc. As such, finding an operation policy in which all constraints are satisfied while energy costs are minimized is a primary objective for water utilities. While many studies tackled the WDS optimal operation problem, most of them used deterministic approaches where all the problem parameters were assumed to be known. Recently, there is a growing interest in decision-making under uncertainty. One promising approach for utilizing optimization under uncertainty is robust optimization (RO) which assures a robust (feasible) solution to any realization of the uncertain parameters, within predefined bounds. In addition, RO does not depend on assuming the existence of probability density functions and is based on tractable formulations that converge to the global optimum. This study suggests utilizing RO to optimize the operation of WDSs under two types of uncertainties: consumers demand uncertainty and pumping cost uncertainty. The method is illustrated on a small illustrative network, showing how these uncertainties impact the operation policies.

Emerging contaminants (ECs) are natural or manufactured chemical compounds that are hard to remove through water treatment; hence, they accumulate in the environment. Such contaminants have already been detected in wastewater, aquatic environments, and water distribution systems (WDSs). Consequently, researchers are developing sensors tailored explicitly to new contaminants. By combining those new sensors with real-time simulation models, digital twins are within reach to assess system-wide water quality. In the future, such twins will build the cornerstone for early warning or real-time control systems concerning these new pollutants. However, realistic simulation tools competent enough to create such digital twins are lacking, mainly because of two reasons: (1) hydraulic models are unable to account for the spatiotemporal dynamics of customer demand, and (2) water quality models are not equipped to simulate the fate and transport of ECs. Our work aims to close this gap by proposing a novel way to model water quality that combines realistic water demand models (i.e., by using the stochastic water demand end-use model, pySIMDEUM), hydraulic solvers (i.e., using the object-oriented Python NETwork analysis tool, OOPNET), and water quality solvers (i.e., using EPyT-C). Extending the state-of-the-art for hydraulic and water quality modeling by incorporating the water demand stochasticity and the uncertainties associated with the imperfect understanding of the formation and transmission of ECs in WDSs is expected to advance the digital twins technology for detecting ECs' formation and evaluating health-related exposure risks in WDSs.

Water distribution systems (WDSs) were studied intensively in the last decades. The research concerning WDS is varied and covers many aspects of the WDS lifecycle such as design, operation, water quality, sensor placement, leak detection, demand forecasting, and more. A common approach in all areas of research on WDSs is to explore within predefined boundaries of the problem and partially represent anything beyond these boundaries. For example, consumers that consume water from the network and use it, a direct outcome of this action is that the sewage collection system gets its input. The two systems are inherently related to each other but analyzed and operated separately. More examples of such boundaries are the connection between WDSs and electricity grids, where electrical prices are usually treated as inputs to WDS problems, but the costs of electricity production and transmission are not considered. Moreover, WDS storage tanks can be used for regulating loads in the electricity grid. Another example is the state (volume, levels, and quality) of water sources such as aquifers and surface waters. These are usually studied from a hydrological perspective, but WDSs and natural water resources are subject to mutual interconnections. These examples (and others) point out that WDSs are not isolated systems; they are integrated with other natural and infrastructure systems. Accordingly, the decision making related to the different aspects of WDSs should consider the full complexity that exists in real systems, which is broader than the WDS itself. This study elaborates on several opportunities to expand the existing boundaries of WDS problems, thus opening new research areas in WDS management.

A key challenge in designing algorithms for leakage detection and isolation in drinking water distribution systems is the performance evaluation and comparison between methodologies using benchmarks. For this purpose, the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) competition was organized in 2020 with the aim to objectively compare the performance of methods for the detection and localization of leakage events, relying on supervisory control and data acquisition (SCADA) measurements of flow and pressure sensors installed within a virtual water distribution system. Several teams from academia and the industry submitted their solutions using various techniques including time series analysis, statistical methods, machine learning, mathematical programming, met-heuristics, and engineering judgment, and were evaluated using realistic economic criteria. This paper summarizes the results of the competition and conducts an analysis of the different leakage detection and isolation methods used by the teams. The competition results highlight the need for further development of methods for leakage detection and isolation, and also the need to develop additional open benchmark problems for this purpose.

A least-cost optimization of a water distribution system (WDS) results in a branched network if some system reliability measures are not considered. Using an explicit level of system redundancy is one way to enhance reliability in the WDS network. This approach consists of first dividing the network into subsystems. Then, during the optimization stage, the subsystems are optimized simultaneously by demanding each one to maintain some level of service. Which pair of subsystems is ultimately selected determines the outcome of the optimization problem. However, there is little if no literature on the optimization of the selection of a pair of subsystems. This study addresses both subsystem and component sizing optimization in designing a level-1 redundant network. Candidate subsystems are enumerated using graph theory where st-numbering for the network is assigned first, and then the backups are generated following the decrement and increment orders of the node's st-numbering.

Water age is the time taken for water to travel through a distribution system and reach the consumer. Generally, there is a trade-off between water pressure and water age in a water distribution system—higher pressure results in higher flow velocity, which often means shorter traveling time for the water, while lower pressure leads to slower flow and thus higher water age. Low pressure is a desired objective in a distribution system, as it reduces the physical stress on its components and minimizes water losses in the event of a leak. Low water age is a desired objective as well, as increased age is regarded as having a low water quality. Therefore, the two objectives compete with one another. The problem of trying to minimize both water pressure and age is a common problem in water distribution systems’ design and management. This paper introduces an algorithm for pressure reducing valves’ (PRVs) placement for reducing water age in water distribution systems. The algorithm is based on graph-theory elements and uses EPANET 2.2 for simulation and analysis. The method is demonstrated on two small scale examples, and the results present relatively significant improvements in respect to water age.

Optimal pressure management is a standard strategy for water loss minimization in water distribution systems (WDS). A pragmatic solution to regulating water pressures and leakage is introducing pressure-reducing valves (PRVs). This paper presents a valve positioning algorithm for optimally deciding the positions and setpoints of PRVs in a WDS. The algorithm derives the hydraulic solution of a WDS as a directed graph, established on the flow directions, using EPANET 2.2 and develops the downstream network supplied by water flowing out of every pipe in the network by applying the depth-first search method. The algorithm later recognizes the pipes leading to the most extended downstream networks, with pressures above the minimum required service pressure, and prioritizes them as the ideal locations for PRV placement. In this way, the proposed algorithm overcomes the limitations of the state-of-the-art in realistically conceptualizing the leakage reduction for optimally positioning the PRVs in WDS. Four studies with varying complexities were selected to demonstrate the algorithm's applicability for deriving pressure management solutions. The solution time for PRV positioning was in seconds for the first three networks and several minutes for the extensive fourth case study. The results corroborate the algorithm's ability to pinpoint the critical nodes with the most increased potential for downstream pressure control and for maintaining the pressure at the least required service pressure level through optimally allocating the PRVs, with acceptable setpoint values, within the pipe network.

The vastness of water distribution systems (WDS) makes them vulnerable to exposure to different types of accidental/intentional contamination. Although most such contamination events that occurred in the recent past were accidental, criminal intent was involved in a few. Considering the accessibility of WDS and the potentially harmful outcomes of drinking-water contamination, online water-quality monitoring sensors are typically positioned in selected locations throughout WDS as a preventive strategy. These sensors, once positioned, communicate over a cyber-infrastructure layer and are liable to cyber-physical attacks—the sensor and/or its communication system becoming compromised or the sensor network becoming malfunctioned such that part of its components is deactivated. However, the sensor network placement state-of-the-art has thus far overlooked these cyber-physical attack scenarios. The current study attempts to overcome this limitation in the state-of-the-art by developing and demonstrating a methodology for evaluating the impact of a cyber-physical attack on a sensor network, compromising its functionality partially. Our proof-of-concept, using a simple network and a straightforward cyber-physical attack scenario, has revealed the vast potential of examining the performance of sensor networks under accidental/intentional malfunctioning and providing valuable information for decision makers in water utilities and regulators.

Past water distribution systems (WDS) management studies derived operation protocols to maximize WDS reliability by using residual chlorine as the sole surrogate parameter for water quality reliability. Albeit the advancement in mechanistic modeling to examine the WDS water quality, emerging water quality parameters of concern are not yet involved in solving WDS management problems. This paper attempts to overcome this limitation by developing a flexible decision-making framework -integrating EPANET-C, a mechanistic modeling tool for WDS water quality, with Analytic Hierarchy Process (AHP), a multi-criteria decision-making method - to rank the possible water quality parameter-based operating alternatives (organic matter and residual chlorine levels at the source points) for WDS. The uncertainty analysis was incorporated into the mechanistic modeling using the Monte Carlo method to realize insufficient knowledge about the complex biological and physicochemical interactions inside WDS. Six cases, each ranking the alternatives diversely, were applied to reflect the expert judgment impressions on the AHP outcomes. The consistency of the proposed decision-making framework was verified by deriving the operation protocol for two test networks by making trade-offs between the multiple and conflicting microbiological, chemical, and organoleptic quality criteria. The disinfection by-products formation control and taste and odor problems control emerged as the most critical water quality criteria determining the WDS performance under the operating alternatives examined. Altogether, the obtained results suggested the practicality of adopting a flexible operation protocol to maintain the water quality benchmarks over various plausible WDS performance scenarios, ranging from worst to best.

At the 2013 World Environmental and Water Resources Congress in Cincinnati, Ohio, the ASCE Task Committee on Research Databases for Water Distribution Systems was formed to develop an online open-access repository of water distribution system hydraulic network files for use in applied scientific research. This paper discusses the development of the resulting database, specific model contributions, available model building toolkits, general methods for system classification, and general information related to the database platform and content. It is hoped that the assembled database will promote the advancement of water distribution research into the next decade.

The optimal design of WDS has been extensively researched for centuries, but most of these studies have employed deterministic optimization models, which are premised on the assumption that the parameters of the design are perfectly known. Given the inherently uncertain nature of many of the WDS design parameters, the results derived from such models may be infeasible or suboptimal when they are implemented in reality due to parameter values that differ from those assumed in the model. Consequently, it is necessary to introduce some uncertainty in the design parameters and find more robust solutions. Robust counterpart optimization is one of the methods used to deal with optimization under uncertainty. In this method, a deterministic data set is derived from an uncertain problem, and a solution is computed such that it remains viable for any data realization within the uncertainty bound. This study adopts the newly emerging robust optimization technique to account for the uncertainty associated with nodal demand in designing water distribution systems using the subsystem-based two-stage approach. Two uncertainty data models with ellipsoidal uncertainty set in consumer demand are examined. The first case, referred to as the uncorrelated problem, considers the assumption that demand uncertainty only affects the mass balance constraint, while the second case, referred to as the correlated case, assumes uncertainty in demand and also propagates to the energy balance constraint.

This study investigates the simulation of celerity attenuation and head damping in transient flows using a Lagrangian approach rather than an Eulerian approach. Typically, the Lagrangian approach requires orders of magnitude fewer calculations, resulting in the rapid solution of very large systems. Additionally, it is based on a simple physical model. As the method is continuous in both time and space, it is less sensitive to the structure of the network and the length of the simulation process. Most recent studies, however, have focused on the development and improvement of computational routines for modeling in an Eulerian environment. This results in the development of adequate models that are suitable for Eulerian models but not applicable in Lagrangian-based models. As a result of this fixation, a bias was created towards using Eulerian approaches in transient simulations. It also diverts resources from further development of Lagrangian models. Consequently, it is necessary to develop a friction model that is more accurate and compatible with Lagrangian methods without compromising their performance. To the authors’ knowledge, such a model is yet to be published in the literature. This study presents a new friction modeling technique that compensates for both the local and convective acceleration terms for the Lagrangian transient modeling approach without compromising the computational time.

The complexity of modeling water quality variations in water distribution systems (WDS), studied for decades, stems from multiple constraints and variables involved and the complexity of the system behavior. The conventional macroscale-based WDS water quality models are founded on continuum mechanics. In attempts to provide a broad picture of the multi-species interactions, these models overlook the stochasticity corresponding to the reaction mechanisms within the WDS domain. Furthermore, owing to the black-box type modeling adopted in simulating the multi-species interactions, the existing state-of-the-art models have limitations in representing intermediates and/or by-products formation. Accordingly, they remain ineffective in describing the water chemistry-stoichiometric interactions within the WDS domain. Only a radically new modeling approach could overcome the limitations of the macroscale-based approaches and enables analyzing the stochastic WDS mechanisms by keeping the true nature of the system behavior. Stimulated by the metabolic network modeling principles in systems biology, this article outlines the prospect of developing an innovative 'water'bolic network modeling approach to provide a new outlook to the existing WDS water quality modeling research.

The multi-objective design optimization of water distribution systems (WDS) is to find the Pareto front of optimal designs of WDS for two or more conflicting design objectives. The most popular conflicting objectives considered for the design of WDS are minimization of cost and maximization of resilience index which are considered for the current study. Robust multi-objective optimization is to find the optimal set of the Pareto front considering demand is uncertain. The robustness is controlled by a single parameter that defines the size of the uncertainty set it can vary. The study explores ellipsoidal uncertainty set with different sizes and co-variance matrices. A combined simulation–optimization framework with a combination of self-adaptive multi-objective cuckoo search (SAMOCSA) and the fmincon optimization algorithm is proposed to solve the robust multi-objective design problem. The proposed algorithm is applied to medium and large WDS. The main contribution of this paper is to study the effect of demand uncertainty and the correlation on the WDS designs in a multi-objective framework. The study shows that the inclusion of correlation into the multi-objective design framework can significantly affect the optimal designs.

The COVID-19 pandemic affected the operation of water utilities across the world. In the context of utilities, new protocols were needed to ensure that employees can work safely, and that water service is not interrupted. This study reports on how the operations of 27 water utilities worldwide were affected by the COVID-19 pandemic. Interviews were conducted between June and October 2020; respondents represent utilities that varied in population size, location, and customer composition (e.g., residential, industrial, commercial, institutional, and university customers). Survey questions focused on the effects of the pandemic on water system operation, demand, revenues, system vulnerabilities, and the use and development of emergency response plans (ERPs). Responses indicate that significant changes in water system operations were implemented to ensure that water utility employees could continue working while maintaining safe social distancing or alternatively working from home. A total of 23 of 27 utilities reported small changes in demand volumes and patterns, which can lead to some changes in water infrastructure operations and water quality. Utilities experienced a range of impacts on finances, where most utilities discussed small decreases in revenues, with a few reporting more drastic impacts. The pandemic revealed new system vulnerabilities, including supply chain management, capacity of staff to perform certain functions remotely, and finances. Some utilities applied existing guidance developed through ERPs with slight modifications, other utilities developed new ERPs to specifically address unique conditions induced by the pandemic, and a few utilities did not use or reference their existing ERPs to change operations. Many utilities suggested that lessons learned would be used in future ERPs, such as personnel training on pandemic risk management or annual mock exercises for preparing employees to better respond to emergencies.

Accurate interpretation of longitudinal dispersion of solutes within distribution pipes increases the reliability of the predictions of water distribution systems (WDS) water quality analysis models. However, the estimation of longitudinal dispersion from fundamental principles is complicated. Thus, a longitudinal diffusion coefficient, a nonphysical constant depending on the flow and pipe properties, is generally applied to characterize the dispersion mechanism during the advective-dispersive-reactive (ADR) modeling in WDS. Many empirical/semiempirical formulas exist for calculating the coefficient values. Several of them have been applied in WDS water quality modeling research also. However, these formulas have shortcomings concerning overestimating and/or underestimating the longitudinal dispersion coefficient value under transitional/turbulent flow regimes and depicting the transient nature of longitudinal dispersion under laminar flow settings. As yet, no effort has been made to comparatively analyze the implications of the performance of these formulas in ADR modeling in WDS. This paper attempted to critically examine the competence of the prevailing state of the art to accurately represent longitudinal dispersion in pipes from a WDS water quality modeling perspective. The results established that the conceptual dissimilarities in incorporating dispersion memory and transient characteristics between the formulas for laminar regimes have significant impacts on regulating the scale of longitudinal dispersion in the ADR model predictions. The relative variances between the formulas for transitional/turbulent flow settings were found comparatively less significant concerning the ADR model outputs. This study's findings can advance the state of the art to minimize the failure risks involved in simulating the concentration profiles when dispersive transport is dominant over advection in WDS.

Typically, computer-based tools built on mathematical models define the time-series behavior of contaminants, in dissolved or colloidal form, within the spatial boundaries of water distribution systems (WDS). EPANET-MSX has become a standard tool for WDS quality modeling due to its collaboration with EPANET. The critical challenges in applying EPANET-MSX include conceptualizing the exchanges among multiple reacting constituents within the WDS domain and developing the scientific descriptions of these exchanges. Moreover, due to its complicated user interface, the EPANET-MSX application demands programming skills from a software engineering viewpoint. The present study aims to overcome these challenges by developing a novel computer-based tool, EPANET-C. Via built-in and customizable conceptual and mathematical models’ directories, EPANET-C simplifies WDS water quality modeling for users, even those lacking programming expertise. Due to its flexibility, EPANET-C can become a de facto standard tool in WDS quality modeling study both for the industry and the academia.

A real-life water distribution system (WDS) contains uncertainty in numerous stages. This makes the optimal management and design of a WDS a complex problem. Water quality has also become a significant factor in the design and management of a WDS. Our objective was to incorporate water quality uncertainty in the WDS design problem. The mixing level was assumed to be uncertain and used to design the WDS such that the design was immune to the level of mixing. This method aimed to yield designs that satisfied the nodal concentration constraints irrespective of the mixing level in the junctions. Two optimization methodologies, robust optimization and info-gap decision theory combined with a cuckoo search optimization algorithm, were proposed to solve this problem. An illustrative example 4×4 grid network was used to understand nonuniform mixing and explain the design methodology using both methodologies. Then these methodologies were applied to solve a similar treatment plant problem on a modified Fossolo network. The results also exhibited a significant variation in cost between complete mixing and nonuniform mixing. The WDS designs obtained from both methods were evaluated through Monte Carlo simulations.

This paper presents a new valve positioning algorithm (VPA) for water network pressure reduction. The proposed algorithm is a graph theory-based algorithm combined with EPANET2.2 to position pressure-reducing valves (PRVs) at the most effective locations, having the most effect on downstream pressure reduction, and then calculates the setpoint for locally controlled valves. Each algorithm solution positions one valve on the edge with the highest-pressure reduction indicator calculated using the depth-first search algorithm. The setpoint is then calculated according to downstream pressure differences to the minimum service pressure to be supplied. The PRV location and setpoint found by the algorithm may be used to install and set locally controlled PRVs or serve as a guide for positioning remote-controlled PRVs. The algorithm is demonstrated on a simple example network using demand-driven analysis (DDA), pressure-driven analysis (PDA), and genetic algorithms (GA) and on a medium and a large example application (DDA). Any number of PRVs may be installed using the algorithm, which returns stable results and very short solution times while maintaining minimum service pressure requirements to the critical consumers.

Contamination events in water distribution systems (WDSs) are highly dangerous events in very vulnerable infrastructure where a quick response by water utility managers is indispensable. Various studies have explored methods to respond to water events and a variety of models have been developed to simulate the consequences and the reactions of all stakeholders involved. This study proposes a novel contamination response and recovery methodology using machine learning and knowledge of the topology and hydraulics of a water network inside of an agent‐based model (ABM). An artificial neural network (ANN) is trained to predict the possible source of the contamination in the network, and the knowledge of the WDS and the possible flow directions throughout a demand pattern is utilized to verify that prediction. The utility manager agent can place mobile sensor equipment to trace the contamination spread after identifying the source to identify endangered and safe places in the water network and communicate that information to the consumer agents through water advisories. The contamination status of the network is continuously updated, and the consumers reaction and decision making are determined by a fuzzy logic system considering their social background, recent stress factors based on findings throughout the COVID‐19 pandemic and their location in the network. The results indicate that the ANN‐based support tool, paired with knowledge of the network, provides a promising support tool for utility managers to identify the source of a possible water event. The optimization of the ANN and the methodology led to accuracies up to 80%, depending on the number of sensors and the prediction types. Furthermore, the specified water advisories according to the mobile sensor placement provide the consumer agents with information on the contamination spread and urges them to seek for help or support less.

This study presents an approximated model for pressure transient simulations as well as a local wall shear stress analysis to control and dislodge biofilm growth in water distribution systems. It demonstrates the potential of taming hydraulic transients to manage and disrupt the growth of biofilm colonies attached to the inner walls of pipeline systems. The systems are subjected to consecutive controlled transient pressure waves by manipulating valves positioned strategically along the water distribution system. Because the controlled transient waves are generated at different locations in the system, the interference properties of the waves can be capitalized on at points where different pressure waves can come together and merge in the system, thus creating high pressures and powerful shear stresses. Nevertheless, it is vital to keep the head pressure confined within the allowed pressure range to avert extreme devastating pressures and ensure the integrity of the system. Three case study applications of increasing complexity are presented to demonstrate the potential of this approach. These transient wave simulations implemented the Lagrangian-based wave characteristic transient model, and the optimization of valve operation was governed by an evolutionary genetic algorithm. The results indicate that it is possible to trigger transient events for biofilm management purposes without exceeding the allowed critical pressure in various network layouts.

This paper investigates the problem of optimal placement and operation of valves and chlorine boosters in water networks. The objective is to minimize average zone pressure while penalizing deviations from target chlorine concentrations. The problem formulation includes nonconvex quadratic terms within constraints representing the energy conservation law for each pipe, and discretized differential equations modeling advective transport of chlorine concentrations. Moreover, binary variables model the placement of valves and chlorine boosters. The resulting optimization problem is a nonconvex mixed integer nonlinear program, which is difficult to solve, especially when large water networks are considered. We develop a new convex heuristic to optimally place and operate valves and chlorine boosters in water networks, while estimating the optimality gaps for the computed solutions. We evaluate the proposed heuristic using case studies with varying sizes and levels of connectivity and complexity, including two large operational water networks. The convex heuristic is shown to generate good-quality feasible solutions in all problem instances with bounds on the optimality gap comparable to the level of uncertainty inherent in hydraulic and water quality models.

The practice of rainwater harvesting (RWH) has been studied extensively in recent years, as it has the potential to alleviate some of the increasing stress on urban water distribution systems and drainage networks. Within the field, an approach of real-time control of rainwater storage is emerging as a method to improve the ability of RWH systems to reduce runoff and urban drainage flows. As applying real-time control on RWH tanks means releasing water that could be used for supply, applying controlled-release policies often hinders the RWH system’s ability to supply water. The suggested study presents an approach that has the potential to improve the capability of a distributed network of RWH systems to mitigate peak drainage flows while substantially reducing the impact on harvested rainwater availability. The suggested method uses a genetic algorithm to generate release policies, which are tailored for any given rain event and initial conditions. The algorithm utilizes the modeled drainage system’s response to a given rainfall pattern and manages to substantially reduce peak drainage flows with little impact on available rainwater when compared to the conventional no-release alternative and other active release methods.

This study presents the potential of integrating Hydrams in modern water distribution systems (WDSs) for managing excess pressure and reducing energy costs. Hydrams, which are also termed Hydraulic ram pumps in the literature, is a cyclic water pump powered by hydropower, generally used to pump drinking and irrigation water in mountainous and rural areas having short of power. The Hydrams is introduced as a sustainable low-cost alternative solution to the more conventional pressure reducing valves (PRVs) approach for managing pressure zones in WDSs. Unlike PRVs, where the pressure is lost and not put into good use, Hydrams mitigate excess pressure at high-pressure zones and direct it to much-needed low-pressure zones. In addition, Hydrams are cheap, simple, environmentally friendly, and require little maintenance. The proposed approach integrates a Hydram in parallel to the original centrifugal pump, where they can be operated interchangeably according to the system’s hydraulic needs. Nevertheless, it is vital to correctly size the Hydram at the feed line and accompany it with a proper storage tank at the low-pressure zone. The storage tank serves as a buffer between the intermittent water supply and consumer demand pattern. Moreover, the tank introduces flexibility into the system that allows more sustainable operating schedules. Two case study applications of increasing complexity are presented to demonstrate the potential of this Hybrid system, later referred to as Hybrid Pumping Unit (HPU). The Hydram and tank sizing is done by a simple heuristic approach, while the operation of the system is dictated by a genetic algorithm. The results demonstrate the potential of integrated Hydrams in reducing excess pressures and energy costs.

Regulatory guidelines implementation for per- and polyfluoroalkyl substances (PFAS) is getting attention globally due to the rising concern about its presence in water sources, direct exposure, and toxic properties. The majority of the legal frameworks primarily emphasize perfluorooctanoic acid (PFOA), an anionic organic PFAS. Latest studies found polyfluoroalkyl amides (FA) as a central precursor to PFOA formation in aquatic systems and established the Hofmann-type rearrangement as the dominant PFOA formation pathway during the chlorination of zwitterionic/cationic FA. Intriguingly, higher PFOA concentrations have been identified in water treatment plants after disinfection also. Hence, there is a probability of transforming FA to toxic PFOA during delivery via water distribution systems (WDS). The PFOA formation in the WDS could become an indirect PFAS exposure source and a probable cause for acute and chronic health risks to the communities. We aim to evaluate the involvement of WDS as an exposure pathway for PFOA. We examined the kinetics of PFOA formation during chlorination in aquatic systems. The kinetic relationships were transformed into mathematical equations and were applied to develop an EPANET-MSX-based WDS quality model. Uncertainty analysis was incorporated into the mechanistic modeling using the Monte Carlo method. The data set obtained on model application to a real WDS was applied for human health risk assessment by calculating the daily intake of PFOA contaminated water using USA population parameters. Our study found the one to eight years old age group to be most susceptible to PFASs exposure beyond the tolerable limit of 3 ng/kg/day.

Water distribution systems are critical infrastructure that deliver high quality drinking water to its consumers. Contamination events in water distribution systems (WDS) are emergencies that can cause distress in the population and require quick response from the responsible utility manager. While regular water quality parameters are monitored at water treatment facilities, it is still a challenge to monitor water quality in the WDS itself. Various models have been developed to explore the reactions and interactions of relevant stakeholders during a contamination event including agent-based modelling. Furthermore, recent research has shown that water demands have significantly changed during the COVID-19 pandemic, and these changes can affect the operation and management of water infrastructure. In this study, an agent-based modelling framework is developed to explore social dynamics and reactions of water consumers and a utility manager during a contamination event, while considering a pandemic demand scenario. Furthermore, innovative response and recovery methods to a contamination event are explored for rehabilitating the water network after a water quality deterioration. Graph theory algorithms are used to place mobile sensor equipment for surveying the water quality in specific network parts, and the distribution system is clustered by the status of endangerment. The Bayesian Belief Network (BBN) was developed using survey data around risk perceptions and social distancing behaviour that were collected during the COVID-19 pandemic. The agent-based model (ABM) was developed using output from the BBN and water use data that were collected during the COVID-19 pandemic. The ABM is coupled with hydraulic simulation of the water infrastructure to evaluate changes in hydraulic performance. The model can be used to explore long and short-term consequences of the pandemic on water distribution systems' management, design, and operations; develop and optimize strategies of how to deal with changes in around water distribution systems due to the pandemic; and investigate how resilient water utilities can cope with additional catastrophic events such as a contamination of a water system during a global or local pandemic related shutdown.

Water distribution systems' (WDS) quality analysis is a complex exercise due to the many constraints and decision variables, the nonlinearity, the non-smoothness of the head-flow-water quality governing equations, and the inter-complexity of WDS behavior. Classically, the WDS quality models operate the time-series output of the hydraulic models, typically EPANET, as its time-series input. Apart from hydraulic input, the other inputs include contamination characteristics (location, type, and duration) and the equilibrium/kinetic relationships defining the reactions within the WDS domain. EPANET-MSX has now become a familiar tool for WDS quality modeling owing to its collaboration with EPANET. The challenging tasks in applying EPANET-MSX are conceptualizing the exchanges between WDS components and water quality parameters and evolving scientific descriptions of the water chemistry stoichiometric interactions. Besides, the EPANET-MSX application necessitates programming skills for successful implementation from a software engineering viewpoint due to its uneasy user interface. We aim to overcome these challenges of applying EPANET - EPANET-MSX for WDS quality modeling by developing EPANET-C, an umbrella simulation platform for simulating numerous accidental/intentional WDS contamination events. It employs a simple command-line interface and avoids the programming requirements almost entirely. EPANET-C synergizes EPANET and EPANET-MSX by employing the in-built and customizable directories of conceptual and mathematical models abstracting the physical, chemical, and biological reactions and designing the equilibrium/kinetic relationships corresponding to several possible WDS contamination scenarios. Due to its flexibility, EPANET-C can become a de facto standard tool in WDS quality modeling study for both the industry and academia.

Water distribution systems (WDSs) function to deliver high-quality water in major quantities. While standard water quality parameters are monitored at waterworks, it is still a challenge to monitor water quality in the WDS network itself. Only hydraulic parameters are frequently monitored and modeled in drinking water networks in Germany. Moreover, the majority of German drinking water utilities does not disinfect when the product leaves the waterworks. This is also common practice in Northern European countries. It is thus important to monitor specific organic and bacteriological water quality parameters which define the system state. This study develops an experimental and simulation integrated framework for continuously monitoring and simulating selected organic and bacteriological water quality parameters, so abnormal deviations in water quality behavior can be detected and responded to in real time. A simple reproducible bacteria regrowth model was taken to initialize the validation of experimental values in a water quality model simulation. Batch experiment measurements from a flow cytometer are analyzed to establish an interface between laboratory values and water quality simulations. Monod kinetics are utilized to describe the bacterial growth rate according to the respective substrate for modeling the bulk species in the water network. Experimental values are incorporated in the simulations for validation. The simulation of bacterial growth is conducted firstly on a network model of a real-life test bed and on various selected distribution system models of different sizes and complexities. First results of the water quality simulations show a successful transition of experimental analysis into water quality simulations and give a promising outlook for developing an online-monitoring and prediction methodology for detecting water quality anomalies efficiently in real time.

This study presents the potential of hydraulic ram pumps in managing excess pressure and reducing energy costs in modern water distribution systems (WDSs). A hydraulic ram pump (or Hydram for short) is a cyclic water pump powered by hydropower, generally used to pump drinking and irrigation water in mountainous and rural areas having short of power. This study introduces Hydrams as a low-cost alternative solution in addition to the more conventional pressure reducing valves (PRVs) approach for managing pressure zones in WDSs. Unlike PRVs, where the pressure is lost and not put into good use, Hydrams mitigate excess pressure at high-pressure zones and direct it to much-needed low-pressure zones. In addition, Hydrams require little maintenance, are cheap, simple, and environmentally friendly. The proposed system integrates a Hydram in parallel to the original centrifugal pump, where they can be operated interchangeably according to the system's hydraulics. Nevertheless, it is vital to correctly size the Hydram at the feed line and accompanying it with a storage tank at the low-pressure zone. The storage tank serves as a buffer between the intermittent water supply and consumer demand pattern. Moreover, the tank introduces flexibility into the system that allows more sustainable operating schedules. Two case study applications of increasing complexity are presented to demonstrate the potential of this approach. The Hydram and tank sizing is done by a simple heuristic approach, while the operation of the system is dictated by a genetic algorithm. The results demonstrate the potential of integrated Hydrams in reducing excess pressures and energy costs.

This study examines the destructive potential of the hydraulic transient in water distribution systems and the role it can play in future cyber-attacks. It demonstrates the use of hydraulic transients to induce great pressure spikes that will compromise the system integrity and disrupt water supply. The purpose of the study is to present a framework where distribution system vulnerabilities are analyzed for better protection against intentional pressure surges. The danger lies in the variety and simplicity of introducing sudden changes to the system, hence inducing pressure surges. In addition, pressure surges travel at high speeds (~1,000 m/s in steel pipes) and propagate throughout the network, thus exposing the whole system to extreme pressures. Therefore, a more thorough analysis of the network is done to examine the vulnerable components and give a better estimation of the network's impregnability. The proposed method excites different transient events along the system and simulates the pressure surge response. The phases between the events can dramatically affect the pressure at different locations. Therefore, the optimal phases for achieving the highest pressure, that is the worst-case scenario, are examined. A case study application is presented to demonstrate the potential of this approach. The results demonstrate the destructive potential of a deliberate hydraulic transient that can cause severe damages and even put the system out of use.

Supplying high-quality water is the key task of water distribution systems (WDSs). Although in Germany and some other countries chlorine is no longer used, it remains as a major disinfectant in WDSs worldwide. Therefore, chlorine concentration represents an important parameter for determining the water quality; that is, we should ensure the chlorine concentration within a reasonable range in a WDS. However, due to the complexity of the network structure and nonlinear behavior of the system, the control of chlorine concentration in WDSs imposes a challenging task. In this study, a model-based optimal control strategy is developed to address this problem. The mass and energy conservation laws are used to describe the hydraulic properties of WDSs. The one-dimensional advective transport model is simplified to describe the decay of chlorine in the pipelines. The chlorine concentration limits at the nodes are formulated as inequality constraints which will be satisfied by manipulating the flows and their directions in the pipelines. As a result, a nonlinear optimization problem is formulated and solved to achieve the specified chlorine concentration. For verifying our approach, we deliver the computed results for benchmark networks as input to the simulation model in EPANET, and the simulation gives satisfactory values of the specified chlorine concentration in the network.

In this manuscript, we investigate the design-for-control problem to optimize locations and operational settings of chlorine boosters in water networks. The objective is to minimize deviations from target chlorine concentrations. The problem formulation includes discretized linear PDEs modeling advective transport of chlorine concentrations. Moreover, binary variables model the placement of chlorine boosters. The resulting optimization problem is a convex mixed integer program (MIP), which is difficult to solve, especially when large water networks are considered. We develop a new swapping heuristic to optimally place and control chlorine boosters in water networks. The proposed method relies on a continuous relaxation of the original MIP. We evaluate the heuristic using two case studies, including one large operational water network from the UK.

Guaranteeing the high-quality of water in water distribution networks (WDSs) is a priority when it comes to ensuring public health. Since the majority of German water utilities (and also other EU countries) do not chlorinate, controlling of nutrients in WDSs such as assimilable organic carbon (AOC) and monitoring of microbiological activities is indispensable. Conventional methods to characterize microbiological activity and dissolved organic matter are time-consuming and labor-intensive. This study presents data on flow cytometry and fluorescence spectroscopy as leading-edge technologies for real-time analysis of drinking water quality in WDS. Flow cytometry is a sensitive method which can be applied in online modus for accurate detection of bacterial cell numbers. Furthermore, fluorescence spectroscopy is a rapid and quantitative technique for detailed characterization of dissolved organic carbon (DOC) including fractions of natural organic matter (NOM). The integration of both techniques is promising for real-time water quality analysis and as a supporting tool for quality control. In the initial step of this research, an experimental laboratory setup of simultaneous analysis is developed. Thus, the goal is to achieve knowledge about possible relations between water quality parameters, more precisely bacterial regrowth potential and the character of dissolved organic constituents. In continuous measurements, the initial state of microbiological growth is to be determined based on flow cytometric data. Due to fluorescence spectroscopy data the microbiological regrowth potential will be predicted. This will be achieved by online detecting of certain organic fractions of the DOC which can be utilized as nutrients by present bacteria. The overall project goal is the establishing of an interface between monitoring parameters and water quality simulation models for the WDSs. First promising results show the successful utilization of adapted AOC as regrowth potential parameter, which can be used for water quality simulations in a WDS.

The purpose of a water distribution network is to supply the required water at sufficient pressures. Water requirement/demand at the nodes is uncertain and can vary erratically. The network design should be able to meet this variation in demand. In addition to this variation in demand, the network should also be resilient to failure. When there is some leakage in a pipe, the network should have surplus energy to meet the required pressure demands at nodes. A new methodology is proposed for the multi-objective combinatorial and discrete water distribution network (WDN) design under uncertainty problem. The proposed methodology uses a combination of popular robust-counterpart (RC) techniques to handle the uncertainty and a meta-heuristic named multi-objective cuckoo search algorithm to handle the discrete combinatorial search space. The uncertain parameter considered here is the nodal demands. The objectives considered for the problem are minimizing construction cost and maximizing resilience index. The proposed methodology is applied to the Hanoi water distribution network. The obtained designs are compared with the standard designs obtained without considering uncertainty. Furthermore, the effect of variation in the uncertainty set of the demands is also reported and discussed.

A water distribution system (WDS) comprises several uncertain parameters. This uncertainty makes the optimal management and design of a WDS a complex problem. Often parameters that explicitly affect water quality are ignored. The contaminant mixing at a junction is assumed to be uniform and instantaneous. Multiple studies prove this assumption wrong, and new water quality modelling methodologies are proposed. The exact computation of the mixing level is complicated and computationally expensive. This study focuses on obtaining the optimal treatment levels at the sources, assuming the mixing levels as uncertain/unknown. A robust optimization approach is proposed to handle this uncertainty. The proposed methodology is explained using an illustrative example 4 × 4 grid network. The objective of the problem is to obtain the water treatment levels at the sources to satisfy the water quality requirements at the demand nodes and be immune to variations in mixing levels at cross junctions. The results showed a significant variation in cost between complete mixing and non-uniform mixing. The obtained treatment levels were verified through Monte-Carlo simulations.

If some measurements of system reliability are not included, a least-cost optimization of a water distribution system (WDS) leads to a branched network. Utilizing network subsystems that provide an explicit level of system redundancy is one way to improve the performance of WDS networks. In this approach, the whole system is subdivided into two subsystem networks. Then, during the optimization phase, the two subsystems are simultaneously optimized so that each can provide some level of service to the consumer. Optimization results are ultimately determined by the pair of subsystems used. Unfortunately, there is little to no literature on the optimization of the selection of subsystems. The present study examines both the selection of subsystems and the optimization of the component sizes in designing a level-1 redundant network. Candidate subsystems are generated using graph theory where st numbering for the network is assigned first, and then the subsystems are generated following the decrement and increment orders of the node's st-numbering. The optimization problem is solved using a genetic algorithm for backup selection and linear programming to compute optimal component sizes.

Water storage tanks are one of the primary and most critical components of water distribution systems (WDSs), which aim to manage water supply by maintaining pressure. In addition, storage provides a surplus source of water in case of an emergency. To gain the mentioned advantages, storage tanks are incorporated in most WDSs. Despite these advantages, storage can also pose negative impacts on water quality, thereby affecting water utilities. Water quality problems are a result of longer residency times and inadequate water mixing. This study aimed to construct a model of a tank’s water quantity and quality by formulating and solving governing equations based on inlet/outlet configurations and processes that influence the movement of water and chemical substances inside it. We used a compartment model to characterize the mixing behavior inside a tank. A water quality simulation model with different compartment arrangements was explored for extended filling and draining of storage, which was further validated using a previously published case study.

In this paper, a new mixed integer nonlinear programming formulation is proposed for optimally placing and operating pressure reducing valves and chlorine booster stations in water distribution networks. The objective is the minimization of average zone pressure, while penalizing deviations from a target chlorine concentration. We propose a relax-tighten-round algorithm based on tightened polyhedral relaxations and a rounding scheme to compute feasible solutions, with bounds on their optimality gaps. This is because off-the-shelf global optimization solvers failed to compute feasible solutions for the considered non-convex mixed integer nonlinear program. The implemented algorithm is evaluated using three benchmarking water networks, and they are shown to outperform off-the-shelf solvers, for these case studies. The proposed heuristic has enabled the computation of good quality feasible solutions in most instances, with bounds on the optimality gaps that are comparable to the order of uncertainty observed in operational water network models.

Drinking water contamination events in water networks are major challenges which require fast handling by the responsible water utility manager agent, and have been explored in a variety of models and scenarios using, e.g., agent-based modelling. This study proposes to use recent findings during the COVID-19 pandemic outbreak and draw analogies regarding responses and reactions to these kinds of challenges. This happens within an agent-based model coupled to a hydraulic simulation where the decision making of the individual agents is based on a fuzzy logic system reacting to a contamination event in a water network. Upon detection of anomalies in the water the utility manager agent places mobile sensor equipment in order to determine endangered areas in the water network and warn the consumer agents. Their actions are determined according to their social backgrounds, location in the water network and possible symptoms from ingesting contaminated water by utilising a fuzzy logic system. Results from an example application suggest that placing mobile equipment and warning consumers in real time is essential as part of a proper response to a contamination event. Furthermore, social background factors such as the age or employment status of the population can play a vital role in the consumer agents’ response to a water event.

Recent studies identified fluoroalkyl amides (FAs) transformation to perfluorooctanoic acid (PFOA) during disinfection as an indirect source of PFASs contamination of drinking water. This paper discerns the position of water disinfection systems (WDSs) as a PFOA exposure pathway. A new mechanistic model incorporating the derived knowledge about the zwitterionic/cationic FAs transformation to PFOA with the unsteady-state hydraulic characteristics of WDSs was developed. The simulation outputs from model application to a WDS from the USA established the significant role of delivery via distribution network in the PFOA formation in drinking water. PFOA exposure risk assessment studies predicted >95% of the system nodes to be at high risk when the existing stringent health-based guideline values are adopted. The 1 to 3 years and 4 to 8 years old age groups were found susceptible to PFOA exposure through drinking water beyond the tolerable limit of 3 ng/kg/day. The model predicted that reducing the chlorine dose from 2±0.2 to 1±0.1 mg/L at the treatment units drops the share of 1 to 3 years old and 4 to 8 years old consumers falling to PFOA exposure from 4.32 to 0.45% and 0.32 to <0.01%, respectively. Besides, 24.9% more, including ∼x223C10% of the consumers of 1 to 3 years old age group, were found exposed to PFOA risks when the organic loading of water was reduced by 60%.

A mechanistic simulation model predicting the response of water distribution systems (WDSs) operated with or without disinfectant residual toward accidental arsenic contamination is developed in this paper. The impacts of chlorination, chloramination, and organic loading to control the oxidation of arsenous acid [As(III)] and the adsorption/desorption of arsenic acid [As(V)] on/from iron pipe walls were simulated by applying the model to two real-world WDSs. The model predicted that during any As(III) contamination event, the arsenic spread in WDSs would engage conservatively in the absence of a residual disinfectant. Due to the swift reactions between chlorine and As(III), maintaining residual chlorine was recognized as an effective strategy to control the soluble As(III) levels. Chloramine was predicted to be less effective than chlorine in causing As(III) oxidation and subsequent As(V) adsorption onto the pipe wall. Besides, under the test conditions considered, the required chloramine dose in the source water had to be 10 times higher to produce equivalent effects in terms of As(III) depletion as the chlorine dose of 1 mg/L. The results presented that chlorine formation in chloraminated WDSs via the monochloramine hydrolysis mechanism contributes to >99% As(III) depletion inside the distribution pipes. Therefore, the paper recommends maintaining additional chloramine residual in chlorinated WDSs to control the As(III) spread during arsenic contamination events in the downstream sections.

Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.

Cyberattacks on critical infrastructure systems are becoming a significant concern. Sabotage and destruction of critical infrastructures may cause devastating impacts on physical systems, economic security, public health, and safety. The water sector is one of the most critical infrastructures, and as such, identifying and managing cyber threats on the water sector's facilities is crucial to providing a continuous and safe supply of water. This study reports on a stakeholder engagement process conducted in the form of an active workshop that aims to show different stakeholder's perspective on cyber threats, knowledge gaps, and barriers to implementations of cybersecurity procedures and technologies in the water sector. The workshop, demonstrated on the Israeli water and cyber sectors, brought together 45 professionals from the water and cyber sectors (government, academia, utilities, consultants, and commercial suppliers). In the cybersecurity domain, many studies focused on developing algorithms, but only a few considered the organizational and policy aspects of the problem. The present study addresses this gap and presents an example demonstrating the importance of integrating stakeholder engagement into decision making in the water domain. The workshop's findings are summarized and analyzed to highlight top priority activity areas and suggest required actions according to the stakeholders' perspective, which can help shape the landscape of the water sector's cybersecurity.

This study aims to develop and solve a multi-objective water distribution systems optimization problem incorporating pumps’ optimal scheduling and leakage minimization. An iterative optimization model was presented for calibrating and computing leakages in water distribution systems to recognize the critical impact of leakage control on system operation. The multi-dimensional and nonlinear optimization model, incorporating pump control, consumer demands, storage, and other water distribution systems’ components, was constructed and was minimized using a multi-objective genetic algorithm coupled with hydraulic simulations. The model was demonstrated on two example applications with increasing complexity through base runs and sensitivity analyses. Results showed that leakage minimization competes against pumping, mainly when significant differences occur between demands during low and high energy tariffs. Pumping during the periods with high electricity tariffs (when the demands are high) generated pressure distribution that decreased the overall leakage related to pump scheduling that replicated the natural inclination to pump as much as possible at low tariffs (when the demands are low). The optimal fronts were found to be very sensitive to the leakage exponent value, and changing its value indeed contradicted the balance between minimizing the leakage and the energy cost significantly. Altogether, the idea presented in this paper was found capable of facilitating the decision-makers to conveniently select between the energy-efficient pump scheduling and pump scheduling reflecting minimum leakage based on the system operator’s preferences. The research also paves the way to rebuild the optimization model by incorporating water distribution reliability and water quality that, in some cases, may also contradict the choice between energy cost and leakage minimization.

A new approach to solving the problem of the optimal operation of a wellfield under water quality constraints is presented and tested. The approach is related to well activation in which all the wells are operated against an approximately constant head. Under these conditions, the hydraulic solutions of the network can be considered as a set of steady-state solutions that depend on the combination of operated wells. This setting is very practical because, in many cases, groups of wells pump water to the same receptor, which maintains an approximate constant head (for example, a relatively large reservoir or a treatment plant). The methodology utilizes mixed-integer linear programming (MILP) to optimize the combinations of wells that will pump the required volume of water given a threshold water quality constraint at minimum energy. This is by dividing the problem into two subproblems of hydraulics and water quality, whose solutions are combined and embedded into a MILP formulation. To overcome the complexity of water quality simulations, a predictive linear regression formulation is utilized to construct linear surrogate connections between the wells and the resulting water quality outcomes, which are then incorporated into a MILP optimization problem. Two example applications of increasing complexity are explored through base runs and sensitivity analyses. The results show that once the model is tuned to a specific water distribution system, an operational plan can be established that provides good approximate results without the need to construct a detailed simulation-evolutionary optimization algorithm framework.

Intrusion of toxic heavy-metal cations into water-distribution systems (WDS) may cause severe adverse health-effects on large populations, along with an undesirable psychological impact. The corrosion (scale) layer, that invariably develops on the pipes’ inner walls, is capable of adsorbing a significant mass of metal-cations and releasing them thereafter via diffusion to the water once operation is resumed, thereby causing a secondary contamination event. To overcome this, the contaminant should be completely removed, in a controlled fashion, from both the aqueous and scale phases, with minimum damage to the pipe’s physical stature. This study determined the range of the Cd(II) adsorption capacity of corrosion-scales and quantified alternative treatments for desorbing it, using an assortment of metal water-pipes, extracted from the WDS. Batch, water-recirculation and flow-through experiments were conducted to determine the extent of Cd(II) adsorption and the best way to desorb it. Corrosion-scales showed substantial Cd(II)-absorption capacity (up to 0.75 mg Cd(II)/g scale) with an approximately linear relation between the aqueous Cd(II) concentration and the adsorbed mass. Desorption experiments included dosages of various acids. Sequential rinsing (eight pipe-volumes) by pH3 solution was found to be the best approach, releasing close to ∼100% of the adsorbed Cd(II), with only a minor effect on the pipes’ integrity.

Steady-state demand-driven water distribution system (WDS) solution is the bedrock for much research conducted in the field related to WDSs. WDSs are modeled using the Darcy–Weisbach equation with the Swamee–Jain equation. However, the Swamee–Jain equation approximates the Colebrook–White equation, errors of which are within 1% for ɛ/D ∈ [10⁻⁶, 10⁻² ] and Re ∈ [5000, 10⁸ ]. A formulation is presented for the solution of WDSs using the Colebrook–White equation. The correctness and efficacy of the head formulation have been demonstrated by applying it to six WDSs with the number of pipes ranges from 454 to 157,044 and the number of nodes ranges from 443 to 150,630. The addition of a physically and fundamentally more accurate WDS solution method can improve the quality of the results achieved in both academic research and industrial application, such as contamination source identification, water hammer analysis, WDS network calibration, sensor placement, and least-cost design and operation of WDSs.

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

This study presents a heuristic multiobjective approach for segmenting and operating water distribution systems (WDS). The methodology employs a two-pronged strategy: the first is a heuristic method for dividing the network into clusters (i.e., district metering areas) based on connectivity analysis. The second is the application of the evolutionary multiobjective optimization method non-dominated sorting genetic algorithm (NSGA)-II for trading off the operational cost, excess pressure (serving as a proxy to leakage reduction), and water age (acting as a surrogate to water quality) in the WDS. Three example applications of increasing complexities with various cluster partitioning are explored, showing a clear trade-off among the objectives. This study introduces an unprecedented heuristic approach for jointly solving the multiobjective problem under a given system partitioning. However, by enforcing a priori clustering formation (rather than including it in the optimization), optimality, completeness, and precision are compromised in favor of computational speed and effort. Thus, additional sensitivities need to be conducted outside of the optimization for the clusters' impact. Challenges of extending this study are in embedding the clusters' formations in the optimization considering other objectives such as residual capacity, developments of other optimization frameworks outside of the generic link of simulation-optimization, and uncertainty inclusion (e.g., in demands). All data and codes are included for allowing full replications and comparisons.

This paper presents an analytical algorithm for simultaneous least-cost design and operation of looped water distribution systems (WDSs). This method could be used to replace evolutionary methods (which are typically used to solve the design-operation problem), or it can be used in conjunction with evolutionary algorithms to enhance their performance (i.e., a hybrid approach). Unlike previous studies that propose analytical methods for split-pipe or continuous diameter design, the developed method addresses a more realistic case in which the pipe design is restricted to commercially available discrete diameters. The analytical approach consists of three stages. In the first stage, a reformulated linear programming (LP) method is used to find the least-cost design of a WDS for a given set of flow distribution while allowing a pipe-split in the solution. In the second stage, the equivalent pipe diameters of the split-pipe design are calculated and modified to discrete pipe diameters by applying a rounding-up strategy to the next commercially available pipe diameter. In the third stage, a nonlinear programming (NLP) method is used to find a new flow distribution that reduces the cost of the WDS operation given the design of the second stage. It is shown in this study that the results produced by the analytical method outperform the results of evolutionary methods when compared to previously published studies. Moreover, when a hybrid approach is adapted, the analytical method can be used to initialize the evolutionary algorithm to gain enhanced performance. The results of the hybrid approach fine-tune those obtained from the analytical method and demonstrate a substantial improvement when compared to a standard evolutionary algorithm initialized with a randomly generated initial population.

2,4,6-trichloroanisole (2,4,6-TCA) formation is often reported as a cause of taste and odor (T&O) problems in water distribution systems (WDSs). The biosynthesis via microbial O-methylation of 2,4,6-trichlorophenol (2,4,6-TCP) is the dominant formation pathway in distribution pipes. This paper attempted to utilize the reported data on the microbial O-methylation process to formulate deterministic kinetic models for explaining 2,4,6-TCA formation dynamics in WDSs. The pipe material’s critical role in stimulating O-methyltransferases enzymatic activity and regulating 2,4,6- TCP bioconversion in water was established. The kinetic expressions formulated were later applied to develop a novel EPANET-MSX-based multi-species reactive-transport (MSRT) model. The effects of operating conditions and temperature in directing the microbiological, chemical, and organoleptic quality variations in WDSs were analyzed using the MSRT model on two benchmark systems. The simulation results specified chlorine application’s implication in maintaining 2,4,6-TCA levels within its perception limit (4 ng/L). In addition, the temperature sensitivity of O-methyltransferases enzymatic activity was described, and the effect of temperature increase from 10 to 25 °C in accelerating the 2,4,6-TCA formation rate in WDSs was explained. Controlling source water 2,4,6-TCP concentration by accepting appropriate treatment techniques was recommended as the primary strategy for regulating the T&O problems in WDSs.

Water distribution networks (WDNs) are critical infrastructure for the welfare of society. Due to their spatial extent and difficulties in deployment of security measures, they are vulnerable to threat scenarios that include the rising concern of cyber-physical attacks. To protect WDNs against different kinds of water contamination, it is customary to deploy water quality (WQ) monitoring sensors. Cyber-attacks on the monitoring system that employs WQ sensors combined with deliberate contamination events via backflow attacks can lead to severe disruptions to water delivery or even potentially fatal consequences for consumers. As such, the water sector is in immediate need of tools and methodologies that can support cyber-physical quality attack simulation and vulnerability assessment of the WQ monitoring system under such attacks. In this study we demonstrate a novel methodology to assess the resilience of placement schemes generated with the Threat Ensemble Vulnerability Assessment and Sensor Placement Optimization Tool (TEVA-SPOT) and evaluated under cyber-physical attacks simulated using the stress-testing platform RISKNOUGHT, using multidimensional metrics and resilience profile graphs. The results of this study show that some sensor designs are inherently more resilient than others, and this trait can be exploited in risk management practices.

A new method for identifying a leaking pipe within a pressurized water distribution system is presented. This novel approach utilizes transient modeling to analyze water networks. Urban water supply networks are important infrastructure that ensures the daily water consumption of urban residents and industrial sites. The aging and deterioration of drinking water mains is the cause of frequent burst pipes, thus making the detection and localization of these bursts a top priority for water distribution companies. Here we describe a novel method based on transient modeling of the water network and produces high-resolution pressure response under various scenarios. Analyzing this data allows the prediction of the leaking pipe. The transient pressure data is classified as leaking pipes or no leak clusters using the K-nearest neighbors (K-NN) algorithm. The transient model requires a massive computation effort to simulate the network’s performance. The classification model presented good performance with an overall accuracy of 0.9 for the basic scenarios. The lowest accuracy was obtained for interpolated scenarios the model had not been trained on; in this case, the accuracy was 0.52.

The increasing risk of intentional, negligent, or accidental intrusion of biological, chemical, or radioactive contaminants in water distribution systems is becoming a major concern that has significant and adverse impacts on public health. As such, it is important to have an effective, robust, and flexible plan that can be readily implemented to minimize the impact of these contamination events. However, limited research has been focused on the strategic planning of the decontamination process of the contaminated infrastructure. This paper proposes an analytical method for modeling the slug-feed method of disinfection given the drainage and disinfectant dosage profiles. The efficacy of the proposed method has been demonstrated by using it as the evaluation function in a genetic algorithm optimization of two case studies. Results show the proposed method exhibits higher robustness compared to the procedures defined in the current standard and literature. The main contributions of this study are to (1) provide a robust and accurate model to describe the slug-feed method of disinfection, (2) offer additional resource utilization flexibility for water authorities, and (3) providing additional levels of district metered areas prioritization.

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorin-ated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mech-anistic model describing the fundamental processes governing the interrelationship between chlo-rine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological pro-cesses that cannot be experimentally determined were neglected. The effects of source water char-acteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were ana-lyzed by applying the model. The results established that a 100% increase in the free chlorine con-centration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining pa-rameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

Contamination events in water distribution systems are emergencies which can cause distress in the population and require fast handling of the responsible utility manager. Various models have been built to explore the reactions of all relevant stakeholders during a contamination event or other emergencies and disasters utilizing agent-based modeling. None of them considers the social background and the possibly related psychological states of the affected population. This study proposes to use recent findings during the COVID-19 pandemic outbreak and draw analogies regarding response and reaction to a disaster on a major scale for implementing it in an agent-based model framework for reacting to a contamination event in a water network. A hydraulic simulation is coupled with an agent-based model which consists of consumer agents and a utility manager. Upon detection of anomalies in the water quality, the utility manager places mobile sensor equipment to emulate "contact tracing," determine endangered areas in the water network, and warn the consumer agents in real time about the geographical spread of the event through, e.g., social media. The consumer agents' actions are determined according to their social backgrounds, location in the water network, and possible symptoms from ingesting contaminated water by utilizing a fuzzy logic system. Results on an example application suggest that placing mobile equipment and warning consumers in real time is essential as a proper response to a contamination event. Furthermore, social background factors like age or employment status of the population can play a vital role in the response of consumer agents to a water quality contamination event in a water distribution system.

The accelerating impacts of climate change pose significant threats to the agriculture sectors. On one hand, the rising summer temperature caused by climate change increases the agricultural water demand due to a higher rate of soil water evaporation and higher crop water demands. On the other hand, a decrease in precipitation amounts and timing threatens the availability of water for rainfed and irrigated agriculture. As such, the current patterns in water usage from existing water resources may soon be insufficient particularly during the warm seasons. Hard adaptation strategies involving infrastructure development and expansion, such as agricultural water systems for water transfers and surface water storage, have the potential to safeguard national and international food security. This study presents a conceptual model to simultaneous design, operation, and layout of an agricultural water distribution systems using robust optimization to deal with the uncertainties in the climate-change impacted agriculture water demand models while considering the impacts on crop yields. An integrated assessment model (IAM), involving climate change projections, a hydrologic model, and a crop production model, is used to determine the water demand at each crop field node. The water demands determined by the IAM can have significant uncertainties due to inherent uncertainties that exist in sub-models and input data. These water demand uncertainties are represented by deterministic variability in robust optimization. Meanwhile, continuous water supplies can incentivize the farmers to adapt to a new and higher yield crop rotation. Therefore, water demand at each node is also correlated to the potential yields, which is bounded above by the water rights assigned to each node. We will demonstrate the use and effectiveness of this approach in the agriculture communities of Umatilla River Basin, Oregon, USA, where water rights, environmental laws, Columbia River Treaty, and overused groundwater aquifers constrain water use and distribution for irrigated agriculture.

This study presents a framework for pressure transient simulation as well as local wall shear stress analysis to manage biofilm growth in water distribution systems. The study aims to disrupt the growth of biofilm colonies attached to the inner walls of pipeline systems. In this approach, the examined systems are subjected to consecutive controlled pressure transient waves. The transient waves are originated by causing sudden changes via valves' manipulation in the distribution system. It is vital to keep the head pressure confined between the allowed pressure range to ensure the integrity of the system. The transient simulation is done by the Lagrangian-based wave characteristic method transient analysis model; two example applications are explored demonstrating the potential of the suggested approach.

This study adopts the concept of over-pressurizing pipelines to manage blockages and other disturbances in pipelines and expands it to a better, more advanced tool. The suggested approach introduces pressure transients into the system and manipulates it to better control the head pressure at a given point along the pipe. Through controlling pressure changes and patterns, we can imitate given pressure signatures, thus dramatically increasing the pressure oscillation and offering different assault patterns to eliminate disturbances along the pipe. Moreover, that is done while averting extreme devastating pressures that can damage the system's components and introduce high pressure for a limited period of time. The suggested approach is simulated by the Lagrangian-based wave characteristic method transient analysis model and demonstrated in a case study for different pressure signatures.

Ensuring the distribution of high-quality water from various sources to consumers via water distribution systems (WDS) is critical for guaranteeing public health. While standard water quality parameters are monitored at waterworks, it is still a challenge to monitor water quality in the WDS itself. A large body of research has investigated where to place online quality sensors in a WDS to detect deterioration in water quality. This study expands prior studies and aims to develop a methodology to determine the location of mobile sensor equipment to monitor water quality change in real-time at strategically important nodes in the water network. A graph-theory algorithm is utilized to determine possible paths from and to the node of the contamination detection. Considering the flow directions and patterns over time, the depth-first search (DFS) is used to explore the fate of the contaminant downstream, exclude possible sources, and to place mobile sensor equipment. By computing sub-graphs and clusters, possible source locations are identified for placing multiple grab samplings in strategically optimized locations, so the source can be detected quickly, and parts of the network can be identified as non-contaminated or as endangered of having deteriorating water quality. By utilizing the physical, topological, and hydraulic properties of the water network, a methodology is developed which enables water utilities to react to contamination events while collecting more information on the fate of the contamination downstream and the state of the water network in real time.

Water distribution system contamination events caused by intentional, negligent, or accidental intrusion of biological, chemical, or radioactive contaminants have significant impacts on the health of the populations that it services. Therefore, it is important to have an effective plan that can be readily implemented to minimize the impact of these contamination events. However, limited research has been focused on strategic planning of the decontamination process of the contaminated infrastructure. This paper proposed a framework for assembling a disinfection plan in real-time by (1) partitioning a WDS into a number of district metered areas (DMAs), (2) generating a solution region for each of the DMAs, and (3) assemble an effective decontamination plan using solution region generated. This framework has been applied to three contamination events. The results show that, when planning for the decontamination stage of a contamination event, the use of the proposed framework can (1) significantly reduce the response time, (2) improve the quality of the decontamination plan, and (3) provide a model for optimizing the resource allocation.

This study presents a critical review of disclosed, documented, and malicious cybersecurity incidents in the water sector to inform safeguarding efforts against cybersecurity threats. The review is presented within a technical context of industrial control system architectures, attack-defense models, and security solutions. Fifteen incidents were selected and analyzed through a search strategy that included a variety of public information sources ranging from federal investigation reports to scientific papers. For each individual incident, the situation, response, remediation, and lessons learned were compiled and described. The findings of this review indicate an increase in the frequency, diversity, and complexity of cyberthreats to the water sector. Although the emergence of new threats, such as ransomware or cryptojacking, was found, a recurrence of similar vulnerabilities and threats, such as insider threats, was also evident, emphasizing the need for an adaptive, cooperative, and comprehensive approach to water cyberdefense.

This paper presents a two-stage method for simultaneous least-cost design and operation of looped water distribution systems (WDSs). After partitioning the network into a chord and spanning trees, in the first stage, a reformulated linear programming (LP) method is used to find the least cost design of a WDS for a given set of flow distribution. In the second stage, a non-linear programming (NLP) method is used to find a new flow distribution that reduces the cost of the WDS operation given the WDS design obtained in stage one. The following features of the proposed two-stage method make it more appealing compared to other methods: (1) the reformulated LP stage can consistently reduce the penalty cost when designing a WDS under multiple loading conditions; (2) robustness as the number of loading conditions increases; (3) parameter tuning is not required; (4) the method reduces the computational burden significantly when compared to meta-heuristic methods; and (5) in oppose to an evolutionary "black box" based methodology such as a genetic algorithm, insights through analytical sensitivity analysis, while the algorithm progresses, are handy. The efficacy of the proposed methodology is demonstrated using two WDSs case studies.

In this paper, we propose a novel methodology for altering the area monitored by water quality sensors in water distribution systems (WDS) when there is suspicion of a contamination event. The proposed active contamination detection (ACD) scheme manipulates WDS actuators, i.e., by closing and opening valves or by changing the set-points at pressure controlled locations to drive flows from specific parts of the network in predetermined paths and enable the sensors to monitor the quality of water from previously unobserved locations. As a consequence, the monitoring coverage of the sensors is increased and some contamination events occurring within those areas can be detected. The objective is to minimize the contamination impact by detecting the contaminant as soon as possible, while also maintaining the hydraulic requirements of the system. Moreover, the methodology facilitates the isolation of the contamination propagation path and its possible source. We demonstrate the ACD scheme on two networks analyze the results and open the discussion for further work in this area.

A water distribution system can be viewed as a graph consisting of nodes and links. Those characterize the consumers and pipes and other network elements such as pipe junctions, valves, sources, tanks, pumps, and reservoirs. In addition to supplying the consumers required consumptions at required pressures, sustaining resiliency, the ability to detect contaminants, sensor leakage minimization, and others, are warranted. Amongst other methods, one of the approaches to accomplish part of these objectives (e.g., leakage control), is clustering (or district metering areas formation). Clustering algorithms vary with network size and objectives and are continuously evolving into practical techniques. The goal of this study is to introduce a new dynamic (i.e., time-varying) clustering methodology for trading-off system objectives such as cost, resiliency, and water quality (quantified through water age). The method is demonstrated on a small illustrative example application and a more complex water distribution system.

The intrusion of a foreign substance into the water distribution system represents a serious threat to public health. Large-scale water distribution systems serve thousands of consumers who may be put at risk to exposure and ingestion of potentially harmful substances. For an authority managing a water distribution system, it is important to (1) detect a potential contamination, and (2) locate the point of intrusion. However, points of known water quality data are expected to be sparsely distributed throughout the water distribution system, and may not provide sufficient data to quickly and accurately localize a contamination event. In this work, an inline mobile sensor was employed for the contamination event localization task in a Bayesian framework, such that the water quality data acquired by the mobile sensor were used to update the contamination intrusion location probabilities in the water distribution system. Using the Bayesian localization method was shown to improve the localization accuracy of a contamination event, with substantial improvements in the precision of localization.

Many studies on pressure sensor (PS) placement and pressure reducing valve (PRV) localization in water distribution systems (WDSs) have been made with the objective of improving water leakage detection and pressure reduction, respectively. However, due to varying operation conditions, it is expected to realize pressure control using a number of PSs and PRVs to keep minimum operating pressure in real-time. This study aims to investigate the PS placement and PRV localization for the purpose of pressure control system design for WDSs. For such a control system, a PS should be positioned to represent the pressure patterns of a region of the WDS. Correspondingly, a PRV should be located to achieve a maximum pressure reduction between two neighboring regions. According to these considerations, an approach based on the k-means++ method for simultaneously determining the numbers and positions of both PSs and PRVs is proposed. Results from three case studies are presented to demonstrate the effectiveness of the suggested approach. It is shown that the sensors positioned have a high accuracy of pressure representation and the valves localized lead to a significant pressure reduction.

Critical water infrastructure is susceptible to various types of major attacks, including direct, human-presence assaults and cyberattacks tampering with industrial control system (ICS) sensors and processes. As attacks become increasingly sophisticated and multifaceted, their timely detection becomes especially challenging and requires the exploitation of different data modalities, such as visual surveillance, channel state information (CSI) from Wi-Fi signals for human-presence detection, and ICS sensor data from the utility.

The 2013 Boston Marathon attack demonstrated the complexity of real-time response to such occurrences. The procedures used by the Federal Bureau of Investigation (FBI) at the Boston event to cluster and mitigate the event consequences inspired the development of a method for real-time response to contamination intrusion events in water distribution systems. Similar to the Boston attack, in the event of water contamination events, the shortage of real-time data, coupled with uncertainties in network topology, water consumption, and the event characteristics, set the ground for the need for a real-time response strategy. A methodology that divides the network into separate monitored zones or clusters, often referred to as district metered areas (DMAs), is widely used to cope with water-related problems such as leakage reduction or pressure control. In this study, a water-quality-related criterion called infection delay time (IDT) was introduced to dynamically cluster the network in case of a contamination event. The IDT parameter was combined with the available system resources to meet water quality goals. A coupled DMA-IDT method was developed for real-time response to contamination events. The setup of the DMA-IDT is described and demonstrated on water distribution systems of various complexities.

This work introduces epanetCPA, an open-source MATLAB^® toolbox for modelling the hydraulic response of water distribution systems to cyber-physical attacks. epanetCPA allows users to quickly design various attack scenarios and assess their impact via simulation with EPANET, a popular public-domain model for water network analysis. The toolbox offers both demand-driven and pressure-driven simulations, enabling the users to realistically analyze cyber-physical attacks and their impacts under both pressure sufficient and pressure deficient conditions. epanetCPA is available under the MIT license.

This study proposes to build upon recent studies on the placement and utilization of water quality sensors in water distribution systems to develop an integrated framework to detect and respond to a potential contamination event. In addition to fixed-location sensors, recently developed mobile sensors can transmit water quality data to assist in the characterization of a contamination event. The resulting information from both sensor types is necessary for plume forecasting and locating the contaminant source, which is required to assist in the remediation of the system. In addition to detecting a potential event, the ability to represent the dynamics associated with a contaminant can further assist in remediation activities. While most efforts have focused on reactions in the bulk fluid, the potential for adsorption of the contaminant on the corroded pipe walls and the potential release of adsorbed contaminants is a threat to the health of the consumer over a long time horizon. Therefore, a method has been developed to estimate the long term desorption of the contaminant cadmium and solutions proposed to integrate this knowledge into an automated long-term recovery system.

Contaminant(s) intrusion into water distribution systems (WDS) may have an adverse effect on large populations. The pipeline corrosion scale has the capability to adsorb the contaminant and thereafter release it to water once the system is returned to operation, causing secondary contamination. Therefore, overcoming contamination events should remove the contaminant from both aqueous and scale phases in a controlled fashion, while not jeopardizing the WDS integrity, nor causing red-water events. This study examined the adsorption and subsequent release of cadmium, as a representative heavy-metal ion, from representative pipeline corrosion scales. Adsorption/desorption batch experiments were conducted on corrosion scales peeled off from old domestic WDS pipes. The effect of water quality (pH, alkalinity, ionic composition, [Cd²⁺]) on Cd(II) adsorption and release was examined. The corrosion scale showed high Cd²⁺absorption capacity and linear relation between [Cd²⁺] and the adsorbed Cd. Desorption experiments included dosages of various acid types, Na2S, and KCl. HCl dosage to pH3 was found a suitable technique, releasing ~90% of the adsorbed Cd(II). The work conclusions will serve to design continuous adsorption/desorption experiments, with the aim of determining the optimal rehabilitation treatment.

Artificial neural network is used to predict development of suspended sediment concentration in annular flume experiments on cohesive sediment erosion. Natural sediment for the experiments was taken from the River Rhine and subjected to a consecutive increase in the bed shear stress. The development of the suspended particulate matter (SPM) was measured and then utilized for artificial neural network training, validation, and testing, including independent testing on new data sets. Several network configurations were examined, in particular, with and without autoregressive input. Additionally, relative importance of auxiliary physical-chemical parameters was analyzed. Artificial neural network with autoregressive input showed very high precision in the SPM prediction for all independent test cases achieving average mean squared error 0.034 and regression value 0.998. It was found that for an abundant training sample, the SPM parameter itself is enough to obtain high quality prediction. At the same time, physical-chemical parameters may provide some improvement to the artificial neural network prediction in cases that comprise values unprecedented in the training sample.

Placing fixed water quality monitoring stations in a water distribution system can greatly improve the security of the system via prompt detection of poor water quality. In the event that a harmful substance is injected into a water distribution system, large populations can be put at risk of exposure to the contaminant. Promptly detecting the presence of a contaminant will reduce the number of people put at risk of exposure. However, to protect against a wide variety of possible contaminants, a water quality monitoring station will need to identify contamination via recognition of anomalous changes in a suite of surrogate water quality indicators (chlorine, pH, etc.). This work attempts to place water quality monitoring stations within the water distribution at locations that best detect contamination events via surrogate water quality signals. Networks of water quality monitoring stations are designed to minimize the population affected prior to contamination event detection, and simultaneously minimize the expected number of false positive detections, under uncertain water quality conditions. Solutions generated in this study are compared to solutions designed via classical detection methods. Results show the sensor networks designed without consideration to detection via surrogate water quality parameters have higher false positive detection rates.

The BATtle of the Attack Detection ALgorithms (BATADAL) is the most recent competition on planning and management of water networks undertaken within the Water Distribution Systems Analysis Symposium. The goal of the battle was to compare the performance of algorithms for the detection of cyber-physical attacks, whose frequency has increased in the last few years along with the adoption of smart water technologies. The design challenge was set for the C-Town network, a real-world, medium-sized water distribution system operated through programmable logic controllers and a supervisory control and data acquisition (SCADA) system. Participants were provided with data sets containing (simulated) SCADA observations, and challenged to design an attack detection algorithm. The effectiveness of all submitted algorithms was evaluated in terms of time-to-detection and classification accuracy. Seven teams participated in the battle and proposed a variety of successful approaches leveraging data analysis, model-based detection mechanisms, and rule checking. Results were presented at the Water Distribution Systems Analysis Symposium (World Environmental and Water Resources Congress) in Sacramento, California on May 21-25, 2017. This paper summarizes the BATADAL problem, proposed algorithms, results, and future research directions.

Using a system to promptly detect anomalous water quality levels in a water distribution system (WDS) is a critical task to ensure security of a public water supply. Using continuous monitoring stations placed at strategic locations throughout a WDS has shown to be an effective method to detect potential contamination or low water quality; however, the performance of these monitoring stations is highly sensitive to the specific locations at which they are placed throughout the network. As a result, a large amount of research has explored how to determine the locations at which to place monitoring stations in a WDS, which may be composed of tens of thousands of junctions and pipes. These studies have typically used explicit simulations of network hydraulics, and contamination events imposed on a water distribution system, to compare how effectively a network of monitoring stations detects simulated contamination events. Building off these previous studies, the work herein proposes a framework to place fixed monitoring stations and input inline mobile sensors to best detect contamination events under uncertain water quality conditions. An adaptive-noisy-multiobjective-messy genetic algorithm is used to efficiently determine the locations at which to place monitoring stations in two sample water distribution systems for minimum cost. Results show that monitoring stations and sensor networks designed within a demand uncertain framework outperform the solutions designed in a deterministic demand framework when evaluated under more realistic demand uncertain conditions.

Simplification methodologies for complex water distribution systems (WDS) are essential for better understanding water distribution system behavior. Such methodologies have substantially improved the management and operation of water distribution systems. WDS are complex structures that may consist of thousands to tens of thousands of elements, which makes their optimal management and operation a very large-scale and complex problem. With the objective of improving the network properties, this work uses mathematical methods drawn from graph theory to represent the water distribution system as a directed graph mimicking the original WDS topology and hydraulic properties. The digraph representation uses graph theory algorithms to identify the unique behaviors on the basis of cluster analysis that distinguishes the specific understandings of WDS. The simulation was performed on two case studies, showing results that were very similar to the results that were predicted theoretically.

This paper focuses on evaluating a scenario-based multiobjective evolutionary algorithm for real-world design problems in which the environment where a system will operate is dynamic, and uncertain. Subsequently, the performance of a stochastic scenario selection scheme, inspired by methods to reduce overfitting in genetic programming, is investigated for scenario-based optimization. Using a scenario-based scheme to address uncertainty in a real-world system's operational environment, system designs are developed via aggregating the performance of a solution evaluated across many scenarios. Within each generation of the evolutionary algorithm the evaluation suite is resampled and evaluated by the current generation's solutions. This scheme is evaluated on two historical noisy test problems and two real-world water resources design problem instances. For each case, the stochastic scenario selection scheme is compared to a static selection scheme at various evaluation suite sizes. Results show the proposed scenario selection scheme to outperform static sampling schemes and increase efficiency of a multiobjective evolutionary algorithm for robust optimization objectives.

In this work, a linear system theory approach produces an analytical solution of contaminant mass transport equations for its subsequent use in multiobjective optimization for optimal waste allocation for a hypothetical multireach, multiwaste source river. The solution scheme used provided contaminant concentrations at the chosen checkpoint at any time, thus allowing for a solution to the optimization problem using a conventional mathematical iterative search method (sequential quadratic programming). The study conceptually investigates the effects of different input waste loads on the optimization with cost and equity objectives. The results suggest that in some cases, equity measure does not protect dischargers downstream, but rather balances all the dischargers with the most restricted dischargers. Moreover, 25-43% fuller utilization of the river's assimilative capacity can be achieved by rearranging input waste loads between the discharges even in comparison with eliminating the most restricted waste sources.

Operational strategies to mitigate combined sewer overflows (CSOs) in older urban areas may be enhanced through real-time decision support provided to sewer operators. During severe rainfall events, real-time hydraulic simulations, coupled with control algorithms, can explore a large number of potential changes to control procedures at short time intervals to provide dynamic feedback and optimization. A model predictive control (MPC) genetic algorithm was developed in previous work and tested offline to explore the efficiency and effectiveness of alternative MPC approaches. This paper extends the MPC methodology to evaluate potential impacts of long-term capital investments on CSO frequency. An alternative strategy to mitigating CSOs in real time with sluice gates may involve replacing small-diameter pipes that cause high hydraulic grade lines throughout the system. CSO reductions may also be significantly enhanced through consideration of larger spatial scales. Replacing conduits is effective but expensive, and optimization over a larger spatial extent (without conduit replacement) has been shown to reduce CSOs by 14%. Optimization over the entire large-scale system is recommended for future work.

This study presents a model for detecting abnormal events in water distribution systems using the minimum volume ellipsoid (MVE) clustered into operation modes, is developed and demonstrated. Estimating multivariate location and scatter while maintaining two important properties, equivariance and positive breakdown, has always been difficult. A well-known estimator, which satisfies both properties, is the Minimum Volume Ellipsoid (MVE) Estimator [1]. The minimum volume ellipsoid (MVE) estimator is the smallest volume ellipsoid that covers m of n observations. The MVE could be found by a resampling algorithm. Its low bias makes the MVE especially useful for outlier detection in multivariate observations [2]. Outliers are observations that locate beyond the minimum volume ellipsoid. Detecting events using outliers is well explained in the Methods section. Water distribution systems operation modes have routine changes; thus some parameters may change between one operation mode and another. Consequently, it may be inaccurate to apply all observations in the same ellipsoid. Clustering the data according to operation modes, then constructing an appropriate MVE for each operation mode is the proposed method to increase the MVE model's accuracy and to obtain more convincing results. The model consists of two major steps: event detection and event classification. The main objective of the first step is to detect anomaly using clustered minimum volume ellipsoid according to operation modes. In the second step, detected abnormal events are classified as physical or quality events. The described model is illustrated by a simple water distribution system.

This Paper focuses on the relationships between pressure and water age. At first basic water distribution network principles are used to drive mathematical relationships. Then through EPANET examples the derived mathematical relations are investigated by using three EPANET water network models as case studies to validate the relationships between pressure and water age. Combined with the model analysis, we conclude that, water age and water leakage can be indirectly controlled through appropriate localization of pressure reducing valves (PRVs).

Water distribution systems are particularly vulnerable infrastructure systems as they comprise numerous exposed elements which can be deliberately infiltrated or may malfunction. Faults in pump operation, errors in sensors, power outages, cyber-attacks, contamination intrusions, and other abnormal complications harm water distribution systems operation in various ways. Therefore, implementing reliable events detection mechanisms in drinking water systems is vital for ensuring societal welfare. As such, the ability to rapidly detect such occurrences (and their origin) is a foremost objective. In this study a model for detecting abnormal events in water distribution systems using a minimum volume ellipsoid (MVE), adapted through time, is developed and demonstrated. A preliminary step to the ellipsoid finding is the clustering of different observation groups. It may be inaccurate to apply all observations in the same ellipsoid, as there are routine changes in water parameters. Applying some seasonal adjustments for example, is essential. Pattern recognition can help characterize typical behaviors of a time series. For example, water temperature rises in 2-3 degrees around 10 am, or increases in chloramine concentration don't last more than 10 minutes. Such characteristics may help define more appropriate ellipsoids for each time step thus generate a more sensitive model. The proposed method repeatedly constructs an MVE and modifies its structure according to new measurements and recorded events. The method is demonstrated on an example application through base runs and sensitivity analyses using hourly consumptions, flows, and pressures.

This paper proposes a multi-objective optimization approach to design pressure driven water distribution systems (WDSs) consisting of numerous elements including pipes, pumps, and storage tanks. The proposed approach includes four stages: mapping the WDS into a network, subdividing the network into segregated district metered areas (DMAs), closing some feed lines which connect the DMAs and re-operating the pumps. The suggested approach solves the multi-objective optimization problem using a genetic algorithm to competitively reduce the water leakage, water age and the operational cost in the WDS, while satisfying customer requirements for minimum pressure, resulting in a Pareto solution chart. The advantages of the proposed approach are illustrated through case studies.

This paper proposes a multi-objective model for the design of a water distribution system (WDS) with numerous elements including pipes, valves, pumps, and storage tanks. The main focus of our decomposition of a WDS to district metered areas is on decreasing water leakage in the system without corrupting the customer service. In other words, the customer demand is to be satisfied and meanwhile the operating pressure and water age will be minimized. The advantages of the proposed model are illustrated through case studies.

In this paper, the differences and similarities between two methodologies aiming to reduce water loss due to leakages in water distribution systems are used to conclude a novel and important statement on leak detection methodologies. The two are then combined to outline a novel methodology incorporating advantages from each method. The outlined combined methodology is demonstrated using one simple example application.

Contamination event detection in Water Distribution Systems (WDS) is an important part of the overall procedures followed by operators to ensure the delivery of safe drinking water to consumers. In practice, detection methodologies may use a small number of sensors, which may be placed optimally throughout the network, to monitor water quality. Typically, due to their high costs, only a small number of these sensors are available within each WDS, and as a result, a part of the network is not monitored by sensors. In this paper, we propose a novel methodology for detecting contamination events by increasing the area monitored by these water quality sensors. Specifically, the proposed methodology can be used in emergency cases when information is available about a possible contamination event in the system, to actively manipulate WDS actuators, by closing and opening valves, and alter the flow directions within the network. This is based on the concept of Active Fault Detection, in which the control input of a system is modified with the aim to improve detectability of a fault. This active detection scheme, drives flows from specific parts of the network in pre-determent paths, to allow the sensors to monitor the quality of water from previously unobserved parts of the network. As a result, the monitoring coverage of the sensors is increased and some contamination events occurring within those areas can be detected. Moreover, the methodology facilitates the isolation of the contamination propagation path and its possible source. We demonstrate how such goals can be achieved on two simple example networks, discuss the benefits of the results and open the discussion for further work in this area.

In this work, we discuss modeling of cyber-physical attacks and their effects on the hydraulic processes of water networks. In particular, we introduce our software epanetCPA, that allows users to quickly develop plausible attack scenarios and assess the hydraulic response of the system. epanetCPA is an open-source MATLABR toolbox, and leverages EPANET for both demand-driven and pressuredriven analysis. The toolbox extends EPANET's features to include a cyber layer of sensors, actuators, controllers, and a centralized SCADA system. The nodes of the cyber network and the attack scenarios to be simulated are defined in an additional input file, which has to be provided along with the EPANET input file describing the physical network. epanetCPA implements a wide range of cyberphysical threats such as denial-of-service of resources, manipulation of sensor readings, as well as eavesdropping, interruption and alteration of the communications between the cyber components.

The Environmental and Water Resources Institute (EWRI) in association with the National Center for Infrastructure Modeling and Management (NCIMM), EPA, and the broad user and open-source software development communities convened an EPANET Visioning Summit in Reston Virginia on April 3-4, 2018. The mission of the summit was to develop a shared vision for the future development of EPANET. There were 35 invited participants including representatives from EWRI, EPA, NCIMM, commercial software companies, engineering consultants, water utilities, academia, and professional organizations. The Summit included keynote plenary presentations, an evening session on successful open-source programs, and a series of focused breakout sessions. Results of this Summit are summarized in this paper.

Looped water distribution system (WDS) repartitioning to district metering areas (DMAs) gained popularity as an effective technique to manage the system and detect and reduce system leakages. However, to apply this method to real WDS, various system properties should be taken into account to ensure efficient water supply. The battle of water networks district meter areas (BWNDMA) is a challenging problem that requires the redesign of the E-Town city network in Colombia. The water utility is looking to repartition the network into manageable DMAs while supplying future demands, keeping minimum and maximum pressures, improving water quality, operating the network at uniform low pressures, balancing water sources, and meeting their seasonal production capabilities. The problem is stated as a multiobjective optimization problem with DMA partitioning being one of eight equal-weighted objectives. They may be reached by (1) closing, opening, or replacing existing pipes, (2) adding parallel pipes, (3) managing storage tanks, pressure valves, and flow-control valves, and (4) utilizing pumps in the dry season. With no known analytical methodology to optimize such a large mixed-integer nonlinear problem, a major difficulty is to find a feasible solution; therefore, a multistage classic engineering approach was taken. First, source allocation and general design were carried out for the operational zones. Then, tank volumes were adjusted to meet their constraints. At this stage, DMAs were introduced to meet pressure regulations. Finally, detailed design and fine-tuning of the operations were carried out. This paper describes the taken procedures and obtained results for the redesign of the E-Town network.

Performance of an early warning system composed of online monitoring sensors for protecting municipal water supply is dependent on the number of sensors deployed. The inherent trade-off of performance versus scale of the system implemented is explored in this paper through multiobjective optimization using an augmented messy genetic algorithm (mGA). The augmented messy GA facilitated the comparison of solutions with variability in the number of sensors deployed. In this paper an early warning system is represented by a system of fixed sensors placed at network junctions, inline mobile sensors deployed from network junctions carried by flow within network pipes, and surface transceivers to communicate wirelessly with mobile sensors for data transmission and analysis. Performance of the implemented early warning system was measured as the time required for contamination detection, the detection likelihood, the population affected prior to event detection, and the total system cost for a small-, medium-, and large-scale municipal network. Results show well-defined Pareto fronts for each objective versus the cost of each solution, providing a tool for designers to optimize budget decisions.

Performance of an early warning system composed of online monitoring sensors for protecting municipal water supply is dependent on the number of sensors deployed. The inherent trade-off of performance versus scale of the system implemented is explored in this paper through multiobjective optimization using an augmented messy genetic algorithm (mGA). The augmented messy GA facilitated the comparison of solutions with variability in the number of sensors deployed. In this paper an early warning system is represented by a system of fixed sensors placed at network junctions, inline mobile sensors deployed from network junctions carried by flow within network pipes, and surface transceivers to communicate wirelessly with mobile sensors for data transmission and analysis. Performance of the implemented early warning system was measured as the time required for contamination detection, the detection likelihood, the population affected prior to event detection, and the total system cost for a small-, medium-, and large-scale municipal network. Results show well-defined Pareto fronts for each objective versus the cost of each solution, providing a tool for designers to optimize budget decisions.

A spatially averaged numerical model was developed to describe the erosion of cohesive sediment. Together with known empirical relations, the model comprises a new formulation for resuspension due to fish activity. Experiments on erosion of natural sediments in the annular flume at Aachen University are used for model calibration. Empirical coefficients were evaluated with genetic algorithms to achieve the best agreement between the model results and the experimental data. The presented model shows sufficient flexibility to account for various sediment properties, including different sediment sources, natural and artificial contaminants, presence or absence of aquatic organisms, and results in an average coefficient of determination, R² = 90.5% between the model results and the experimental data. Model validation allows it to be assumed that different contaminants affect bed properties differently. Fish activity plays an essential role in correct resuspension prediction. Further sediment erosion experiments with carefully chosen conditions will allow a more comprehensive model evaluation. The presented model is intended to serve as a building block in the development of a hydraulic-sediment-biota model within the W3-Hydro: Water Quality Event Detection for Urban Water Security and Urban Water Management Based on Hydrotoxicological Investigations project that aims to improve the knowledge concerning bioavailability, transport, fate, and effects of contaminants on the aquatic environment.

In the past decades, bioassays and whole-organism bioassay have become important tools not only in compliance testing of industrial chemicals and plant protection products, but also in the monitoring of environmental quality. With few exceptions, such test systems are discontinuous. They require exposure of the biological test material in small units, such as multiwell plates, during prolonged incubation periods, and do not allow online read-outs. It is mostly due to these shortcomings that applications in continuous monitoring of, e.g., drinking or surface water quality are largely missing. We propose the use of pipetting robots that can be used to automatically exchange samples in multiwell plates with fresh samples in a semi-static manner, as a potential solution to overcome these limitations. In this study, we developed a simple and low-cost, versatile pipetting robot constructed partly using open-source hardware that has a small footprint and can be used for online monitoring of water quality by means of an automated whole-organism bioassay. We tested its precision in automated 2-fold dilution series and used it for exposure of zebrafish embryos (Danio rerio)–a common model species in ecotoxicology—to cadmium chloride and permethrin. We found that, compared to conventional static or semi-static exposure scenarios, effects of the two chemicals in zebrafish embryos generally occurred at lower concentrations, and analytically verified that the increased frequency of media exchange resulted in a greater availability of the chemical. In combination with advanced detection systems this custom-made pipetting robot has the potential to become a valuable tool in future monitoring strategies for drinking and surface water.

This work contributes a modeling framework to characterize the effect of cyber-physical attacks (CPAs) on the hydraulic behavior of water distribution systems. The framework consists of an attack model and a MATLAB toolbox named epanetCPA. The former identifies the components of the cyber infrastructure (e.g., sensors or programmable logic controllers) that are potentially vulnerable to attacks, whereas the latter allows determining the exact specifications of an attack (e.g., timing or duration) and simulating it with EPANET. The framework is applied to C-Town network for a broad range of illustrative attack scenarios. Results show that the hydraulic response of the network to a cyber-physical attack depends not only on the attack specifications, but also on the system initial conditions and demand at the junctions. It is also found that the same hydraulic response can be obtained by implementing completely different attacks. This has some important implications on the design of attack detection mechanisms, which should identify anomalous behaviors in a water network as well as the cyber components being hacked. Finally, the manuscript presents some ideas regarding the next steps needed to thoroughly assess the risk of cyber attacks on water distribution systems.

Prompt detection of intentional or accidental contamination of the public water supply is vital to maintain public health in any centralized water distribution system. Being able to quickly detect a system contamination event may be the single most influential factor to reduce possible contamination fallout. Consequently, major research has explored how to best protect a water distribution system (WDS) through strategic placement of fixed water quality monitoring stations. Although fixed monitoring stations within a wireless sensor network (WSN) are robust with respect to hydraulic conditions, the stations are expensive to place, and may not provide the highest spatial and temporal resolution of contamination detection. This work sets to build the understanding of a mobile wireless sensor network (MWSN) where inline mobile sensors function within water in pipes to monitor water quality and to wirelessly transmit data to fixed transceivers in real time. Mobile sensor behavior was modeled alongside contamination simulations and the deployment of fixed and mobile sensors was together optimized to minimize the affected population prior contamination event detection constrained by a total system cost. Results show a MWSN to be highly sensitive to sensor battery life, transceiver network coverage, and total system cost. Future obstacles for implementation of a MWSN are highlighted and discussed to be address in future work.

The ability to provide high water quality distribution from sources to consumers is a foremost objective in water distribution systems security. Different techniques based on physical, chemical, and other methods constitute a variety of solutions for the layout, design, and operation of water networks, all aimed at decreasing the risk of supplying contaminated water to consumers. This study presents a new analysis approach based on clustering insights of water distribution networks connectivity for enhancing its security. Clustering is utilized herein for dynamically exploring the system behavior. Through clustering formations, addition of network components such as pipes and tanks for the layout problem, and valves for its operation-contamination spreading is controlled and contained. The developed methodology is demonstrated on several example applications, showing its capabilities to provide a decision support tool for contaminant control and containment in water distribution systems.

This study presents a physarum polycephalum-inspired mathematical model for the solution of the problem of least cost design of water distribution systems. We propose modifications of the classical physarum polycephalum mathematical model to adjust it for water distribution system optimization. The methodology was tested on two small-scale benchmark examples: two-loop and Hanoi networks. In the both cases, the obtained results are 10-11% above the known optimal solution, however, the number of iterations required to achieve them are exceptionally small. The proposed approach should be further tested for its applicability to the larger networks. Altogether, the method can serve as a good and easily obtainable first approximation for the least cost water distribution system design.

In water distribution system operation problems, a solution is often, if not always, valid for a small variety of forecasted scenarios. The number of unknown variables, such as consumer consumptions, pipe smoothness, leaks or infections makes the forecasting of the conditions in which the network operates almost impossible. On the other end, most of the solution procedures take use of deterministic models such as water and energy balance or demand patterns - and even stochastic models cannot predict the conditions for which the problem in hand is defined. This procedure of using exact solution models for inexact problems is reconsidered while a methodology of using clusters in water distribution systems is demonstrated on selected regions, utilizing simple examples as an effective tool for bridging the gap.

A new dynamic methodology is presented to attack network clustering and network aggregation problems. The aim of water network clustering and aggregation is to have better understanding of water distribution and network behavior and assist in the development of engineering tools to increase network optimal performance, supply quality and water safety. Clustering techniques take use of the water network topology and flow regime to group nodes with pre-determent properties in such way that a new, aggregated and simplified, network is created without the loss of network hydraulic characteristics and performance. A connectivity based methodology for network clustering is chosen in this work as the clustering method and a new dynamic approach is demonstrated through a series of cases and algorithms on a sample network.

The design of water resource management and control systems have provided a promising space for evolutionary algorithms. In many cases a system for managing a water resource requires a large degree of planning and design before implementation and many stake holders perceive different objectives with different importance. Multiobjective evolutionary algorithms inherently provide a tool that can best satisfy the desires of many stakeholders (many objectives) through computation of a non-dominated solution set. However, the performance of an optimal solution provided by a multiobjective evolutionary algorithm is likely to deteriorate during real-world implementation if design conditions of the optimization framework are not identical to those imposed on the system in practice. This paper focuses on evaluating a scenario based multiobjective evolutionary algorithm for real-world design problems in which the environment where a system will operate is dynamic, and uncertain. A previously developed genetic algorithm termed the "RNSGA-II" used for water distribution system design is augmented to incorporate robust objectives and simple Monte Carlo sampling to solve the classic water quality sensor placement problem. This study aims to further develop an understanding of scenario based optimization methods for optimizing solutions to perform well in the face of uncertainty.

A water distribution system is designed to deliver sufficient quantities of high quality water to consumers. The quality of water delivered in a WDS is difficult to maintain and monitor, as water quality can degrade within a water distribution system's pipes. A network of online monitoring stations designed to sample water quality in real-time, and to be strategically placed in a water distribution system has proven an effective method to monitor water quality and provide early warning of a contaminant intrusion. A difficult task for an online monitoring station is to distinguish natural variability in water quality from variability caused by the presence of a contaminant in the water. Prior studies have shown how common water quality indicators (free chlorine, pH, conductivity, total organic carbon) respond to contaminants, and methods to ease the task of recognizing true contaminant presence. This work proposes an objective function incorporating the uncertainty in a monitoring station's detection employed in the framework of a genetic algorithm to place sensors at locations which minimize the expected consequence of a contamination event, and where water quality data are most indicative of true contamination events. Early warning systems composed of fixed water quality stations and inline mobile water quality sensors are designed in a multi-objective framework for a sample water distribution system.

The interaction between sediment and surrounding water is influenced by numerous physical-chemical parameters. The presented sediment transport model allows predicting suspended solids concentration in water phase as a function of applied shear stress, sediment grain diameter, and fish abundance measure. Experiments on erosion of natural sediments in the annular flume at RWTH Aachen University are used for model calibration. Empirical coefficients are evaluated with genetic algorithms to achieve the best model fit. The presented model shows enough flexibility to account for various sediment properties, including different sediment sources, natural and artificial contaminants, presence or absence of aquatic organisms, and results in average determination coefficient R² = 90.4% between the model and the experimental data. Model simulation outcomes clearly show the essential to account for fish activity to predict the resuspension course correctly. Further sediment erosion experiments with carefully chosen conditions will allow more comprehensive model adjustment.

A goal of water distribution system security is to ensure that clean water is delivered to consumers. Potential for contaminations and cross connections make it difficult to ensure that the water being delivered to consumers is truly of high quality. The placement of water quality monitoring stations within a WDS system has proven a successful method to prevent the delivery of contaminated water. The locations of monitoring stations in a WDS is critical to the performance of a water quality monitoring station network (early warning system or EWS); ideally sensors will be placed at a limited number of locations which can quickly detect all contamination events. Designing a EWS to protect against every possible contamination event is computationally infeasible; however it is crucial that high impact contamination events will be detected. In this study a contamination event is defined as an intrusion taking place at a specific junction and time in a WDS. A probability distribution is generated according to the portion of a network's population served by water that flows "downstream" from a specific junction at a specific time, within a defined interval of time; this portion of population would be most at risk to exposure of the corresponding junction and time. The newly generated probability distribution is then used for sampling a set of contamination events used to design an EWS. The downstream nodes are calculated using breadth first search and the nodal populations are calculated according to the temporal demands. The relative consequence of a junction being contaminated is calculated using steady state and dynamic hydraulic models; elevating the need to perform numerous complex water quality simulations. Monitoring station networks designed using the proposed importance sampling technique and traditional random sampling are compared.

Water distribution systems are vulnerable of being intentionally or accidentally contaminated. In the case of a contamination event the contaminated section of the water distribution system should be located, isolated and cleaned before it can be returned to regular service. Depending on the specific contaminant, the cleaning process mainly includes flushing and disinfection. All of the disinfection methods (for example: tablet, continuous and slug) require that the disinfectants will have minimal contact time (T) in a predefined concentration (C) with the pipes and the system's apparatuses. The regulatory agencies, such as the Ministry of Health in Israel or the American Water Work Association, publishes procedures for the disinfection of water systems in which usually only a single water main is considered and no specific procedures are given for larger portions of the water system. This paper presents an optimal operation plan for the disinfection of water distribution systems using the slug feed method while considering the injection times, flow rates and drainage locations while keeping variable disinfection C∗T values in different areas of the network. The method is a two stage GA-EPANET procedure. It is demonstrated on a small real-world network section.

Incorporating a system of monitoring stations to insure high quality water is being delivered to consumers has been acknowledged a crucial component required by any public water distribution system (WDS). Extensive studies have acknowledged the risk posed to large populations by an accidental or intentional contamination intrusion within a WDS; failure of an early warning system (EWS) to report a contamination event carries profound economic and public health consequences. Dynamic, stochastic conditions exist in municipal WDSs and a monitoring system needs to be designed according to a robust protocol that incorporates the inherent uncertainty in WDS operation, including: demand variability, and contamination event characteristic variability. This work composes the problem of locating the best junctions within a WDS to place fixed monitoring stations, and the best junctions to input innovative inline mobile sensors, in a multi-objective framework that incorporates uncertainty in the network's demands and EWS operation. Mobile sensors are carried by flow within pipes sampling and monitoring water quality in real time, and wirelessly uploading data to fixed transceiver beacons, providing an implicit preference towards demand dense regions. A multi-objective noisy messy genetic algorithm is structured to the problem at hand and employed on a small, medium, and large-scale model WDS to calculate near-optimal solutions from the large solutions space. This multi-objective framework provides high performing trade off (Pareto) sets comparing an EWS's system cost to numerous performance objectives incorporating non-deterministic objective functions to provide a high performing and resilient EWS. Results show a large trade off surface between the cost and the respective system's performance, with large diminishing returns. Although implementing a more expensive solution may provide little to no benefit from a traditional performance standpoint, implementing a system of higher cost can increase the systems resiliency, highlighting the importance of incorporating proper objective measures in optimization procedure.

In water distribution systems, the prediction of infection spread along the system's components is critical both for minimizing impact on the system population and for real-time response. Delaying the overall spread of an infection from a single or from multiple infection sources can be a very useful tool for handling such contamination scenarios. In this study, a new parameter for Infection Delay Time (IDT) is presented and the methodology utilizing it is presented on simple and complex networks, along with highlighting the IDT advantages and applications for system's design and operation.

The intrusion of an unknown substance within a water distribution system can dramatically disrupt the ability to deliver sufficient quantities of clean water. Although prompt detection can be considered the most important matter to protect public from ingestion of unknown substances, it is equally critical that a managing authority locate the intrusion, and return the network to standard operation. This study investigates the use of previously studied inline mobile water quality sensors in an "on-demand" fashion to localize potential contamination intrusion locations. This study employs mobile sensors to traverse through water distribution system pipes along paths that provide the most new information about potential source location contamination statuses. Using network connectivity, upstream regions of nodes traversed by the mobile sensors are deemed either a feasible or infeasible intrusion region based on the contamination status determined by the mobile sensor. Successively, mobile sensors are input until the region of potential intrusion is smaller than a defined threshold number of network nodes. Results of the mobile sensors employed for contamination intrusion localization are compared to the fixed sensor information. This study represents a preliminary investigation of a simple heuristic method to best deploy mobile water quality sensors for contamination event localization.

This work presents an algorithm for water distribution systems water age clustering. The objective is to cluster a distribution system into water age sub-zones whose water age variability is minimized within each cluster. The algorithm stages are: (1) water age computation for each system node, (2) kick-off at a number of clusters equal to the number of nodes (i.e., each node initially acts as a cluster), (3) search for the two connected (by link) clusters which have the smallest absolute water age difference, and combine them into a single cluster; characterize their water age value as the weighted arithmetic mean of the two clusters, and (4) repeat step 3 until all nodes are lumped into a single cluster (i.e., the entire water distribution system). The algorithm thus spans all possible clusters starting from the total number of system nodes and up to a one cluster which holds the entire system layout. The model, through a clustering numbering trade-off, is demonstrated on a mid-size water distribution system.

An effective biological early warning system for the detection of water contamination should employ undemanding species that rapidly react to the presence of contaminants in their environment. The demonstrated reaction should be comprehensible and unambiguously evidential of the contamination event. This study utilized 96 h post fertilization zebrafish larvae and tested their behavioral response to acute exposure to low concentrations of cadmium chloride (CdCl₂) (5.0, 2.5, 1.25, 0.625 mg/L) and permethrin (0.05, 0.029, 0.017, 0.01 μg/L). We hypothesize that the number of larvae that show advanced trajectories in a group corresponds with water contamination, as the latter triggers avoidance behavior in the organisms. The proportion of advanced trajectories in the control and treated groups during the first minute of darkness was designated as a segregation parameter. It was parametrized and a threshold value was set using one CdCl₂ trial and then applied to the remaining CdCl₂ and permethrin replicates. For all cases, the method allowed distinguishing between the control and treated groups within two cycles of light: dark. The calculated parameter was statistically significantly different between the treated and control groups, except for the lowest CdCl₂ concentration (0.625 mg/L) in one replicate. This proof-of-concept study shows the potential of the proposed methodology for utilization as part of a multispecies biomonitoring system.

Least-cost operation of water distribution systems (WDS) is a well-known problem in water distribution systems optimization. The formulation of the problem started with deterministic modeling, and the problem was subsequently handled with more sophisticated stochastic models that incorporate uncertainties related to the problem's parameters. This work applied a recently developed algorithm entitled limited multistage stochastic programming (LMSP) to deal with the stochastic formulation of the least-cost operation of WDS and serves merely as a proof of concept on an illustrative network. The demand is considered as the uncertain parameter in the problem formulation. This algorithm reduces the complexity of the classical multistage stochastic programming (MSP) by adding constraints which result in a linear growth of the problem, as opposed to an exponential growth in the MSP problem. This is accomplished by clustering decision variables based on a postanalysis of the implicit stochastic program of the problem. The clusters allow reduction of the number of decision variables, thus reducing the complexity of the optimization problem. The LMSP is expected to increase the cost because of the additional constraints imposed on the problem; however, a trade-off exists between the computational complexity and the optimality of the objective value to the number of clusters considered. An illustrative example application is provided for demonstrating the suggested methodology abilities.

Finding the optimal pump operation in water distribution systems, taking into account hydraulic and water quality constraints, is a complex problem due to the nonlinear relationship between dynamic head loss and flow rate and between chlorine decay and water age, and due to the size of the problem. The proposed algorithm, for minimum cost pump scheduling, utilizes the operational graph algorithm applied to hydraulic and quality constraints. The proposed algorithm utilizes a graph algorithm that considers hydraulic and water quality constraints to find the pump scheduling that minimizes pump operational costs. The algorithm has short solution times and therefore is suitable for real-time water system control or, if used offline, for giving a recommendation on the pump operation taking into account hydraulic and water quality constraints. The algorithm results were compared with the best results found by enumeration to show that the operational graph algorithm returns a global minimal solution and, when combined with quality constraints, returns near-optimal results. The proposed algorithm was successfully demonstrated on a 24-h example application with a single pumping unit and, on the C-Town example application, with 11 pumping units and 168 time steps.

Least-cost design of water distribution system is a well-known problem in the literature. The formulation of the least-cost design problem started by deterministic modeling and later by more sophisticated stochastic models that incorporate uncertainties related to the problem's parameters. Recently, a new nonprobabilistic modeling, titled the robust counterpart (RC) approach, has been developed for the least-cost design problem to incorporate the uncertainty without the need for full stochastic information. These nonprobabilistic methods, developed in the field of robust optimization, were shown to be advantageous over classical stochastic methods in the following aspects: tractability and computation time, nonnecessity of full probabilistic information, and the ability to integrate correlation of uncertain parameters aspects without adding complexity. Former studies have considered the RC approach for a special case of the least-cost problem with a single load demand uncertainty, and single gravitational source to simplify the problem formulation and facilitate the use of the method. This special case does not handle the joint temporal and spatial correlations between the problem uncertainties and does not include components such as pumping stations and storage facilities. These new components require trading off capital and operation (i.e., energy) costs in the objective function, as the design cost is explicitly influenced by the demand uncertainty, unlike the situation where only capital cost is considered. In this study, the RC approach is expanded to cover the general least-cost design problem, including (1) multiload patterns, (2) pumping stations, and (3) storage facilities. The unknowns are the pipe diameters, pump and tank capacities, and the heads added by the pumping stations. The problem is solved using the cross-entropy method for several possible protection levels, which are defined by the size of the uncertainty set. The results are demonstrated on two examples to show the trade-off between cost and reliability and test the network's ability to cope with unexpected scenarios.

Leak and backflow detections are essential aspects of Water Distribution Systems (WDSs) monitoring and are commonly fulfilled using approaches that are based on static sensor networks and point measurements. Alternatively, we propose a mobile, wireless sensor network solution composed of mobile sensor nodes that travel freely inside the pipes with the water flow, collect and transmit measurements in near-realtime (called sensors) and static access points (called beacons). This study complements the tremendous progress in mobile sensor technology. We formulate the sensor and beacon optimal placement task as a Mixed Integer Nonlinear Programming (MINLP) problem to maximize localization accuracy with budget constraint. Given the high time complexity of MINLP formulation, we propose a disjoint scheme that follows the strategy of splitting the sensor and beacon placement problems and determining the respective number of sensors and beacons by exhaustive search in linear time.

The battle of background leakage assessment for water networks (BBLAWN) challenge was approached using successive linear programming. A linear representation was solved successively for the nonlinear constraints of headloss, leakage, pump energy consumption, and pipe sizing. The optimization model returned minimal cost pump scheduling and pipe sizing while minimizing leakage and maintaining minimum service pressures to the consumers. Pressure reducing valves, pump, and water tank sizing were performed manually and their effect was examined using the optimization model. Parallel pipes were added along the main supply pipes from the pumping stations to the water tanks, to allow for minimum service pressures. Pressure reducing valves were added to pipes branching from the main supply pipes to lower excess pressures to the secondary supply pipes.

The effect of downstream valve closure scheduling was analyzed to find optimal closure parameters that lead to the minimal maximum pressure head in the water distribution system. Several valve closure strategies were explored, combining the known valve performance curve (change in flow as a function of change in valve's opened area) with unknown valve closure curve (change in valve's opened area as a function of time). Second-order polynomial curve, power function curve, and piecewise linear curve were implemented and compared. Genetic algorithm and quasi-Newton (QN) optimization methods were applied. The methodology was tested for three networks, including looped gravitational and pressurized networks. The results demonstrate that flexible multiparametric valve closure curve and QN optimization method are more effective in minimizing the maximum pressure head in the system.

A graph theory-based algorithm is demonstrated for optimal pump scheduling of two example application water networks. The hydraulic part of the problem is solved using a dedicated and efficient hydraulic solver. The pump scheduling part of the problem is solved using a skeletonized operational graph, representing only the basic logic operational relations existing in the network required for pump selection: the pumping units (with nominal operating costs), water tanks and clustered demand nodes. The hydraulic solver advances one time step at a time. After each time step advance, the nodes of the model are checked to see if satisfy minimum service pressure and minimum water tank level. For nodes not satisfying the service constraints, the Dijkstra's shortest path algorithm is applied to the skeletonized graph to determine the optimal pumping unit to be activated and then updating the pumps operation pattern in the model. The hydraulic solver is then reinitialized to resolve and recheck the time steps one by one. The algorithm ends when the solver reaches the last time step with all nodes meeting service constraints. The algorithm returns an optimal minimal cost pump-scheduling pattern under greater-than constraints over the examined time period, such as (1) minimal consumer service pressure, and (2) water balance closure at the water tanks. The algorithm returns discrete pump operation scheduling with minimal pump switching and minimal water age in the tanks, demonstrating short solution times (28 s to schedule 11 pumps over a 168-hour period). The algorithm may be applicable for real-time pump scheduling. Future research may include water quality constraints and variable frequency drive pump scheduling. Pump selection is based on the assumption that the optimal pump operation order is not affected by changes in the network's hydraulic conditions, such as water tank levels and location along the pump efficiency curve (assuming constant efficiency). If hydraulic conditions change the optimal activation order, then the pumps' working points must by updated after each time step solution, which is not addressed in the current work.

This study deals with the integration of contamination simulations and a spatial event detection model. The simulation of contaminant intrusion includes detailed chemical-specific reactions within a multi-species water quality model. This set-up generates a scenario of contaminant distribution and produces a continuous multiple sensor stations database. Three organophosphates pesticides, Chlorpyrifos, Malathion, and Parathion, are modeled as possible contaminants. The event detection model comprises both local and spatial data analysis. The local model applies a previously developed single-sensor event detection model with a higher alert threshold that reduces false alarm rates. The spatial model considers upstream sensor datasets which are examined for their uniqueness and mutual resemblance in a sliding time window. The model utilizes outlier detection, data analyses, and network hydraulics for the detection of suspicious spatial trends. The proposed algorithm is capable of detecting events with low contamination signatures and spatial influence. Two case studies are explored and compared to the single sensor model. The proposed methodology resulted in a lower number of false alarms compared to the previous single sensor event detection modeling approach.

Optimal sensor placement for detecting contamination events in water distribution systems is a well explored problem in water distribution systems security. We study herein the problem of sensor placement in water networks to minimize the consumption of contaminated water prior to contamination detection. For any sensor placement, the average consumption of contaminated water prior to event detection amongst all simulated events is employed as the sensing performance metric. A branch and bound sensor placement algorithm is proposed based on greedy heuristics and convex relaxation. Compared to the state of the art results of the battle of the water sensor networks (BWSN) study, the proposed methodology demonstrated a significant performance enhancement, in particular by applying greedy heuristics to repeated sampling of random subsets of events.

This study describes a binary integer programming model for mutually-operating fixed and mobile sensors in water distribution systems. The proposed method applies a deterministic optimization scheme for maximizing the monitored volume within network clusters. For a given budget, the model determines the ratio of mobile sensors to fixed sensors along with their placement and release strategies. Through assessing the benefit of placing each fixed sensor and the time and location of mobile sensors release, the combination of fixed and mobile sensors is determined. Utilizing mobile sensors for water quality monitoring is still in its infancy. Such sensors are equipped with self-powered sensing, sampling, data acquisition, and wireless transmission units. The model initiates with the combined operation of mobile and fixed sensors. It then explores the benefits of mobile sensors compared to fixed. The two battle of the water sensor networks (BWSN) are utilized for demonstrating the model's capabilities. Mobile sensors are found to be beneficial to water distribution system monitoring when operated in conjunction with static sensors.

Optimal water system operation, including hydraulic and water quality constraints are complex problems to solve due to the nonlinear relationship of head-loss to flow and disinfectant-loss to time. A common approach used today is evolutionary algorithms (EA) such as genetic algorithms or others. For large problems with a large number of decision variables, ether over extended period or having several pumping units, the extended solution time of the EA approach may render the approach not relevant. The proposed method utilized the operation graph optimization (OGO) algorithm proposed previously by the authors, demonstrating high speed discrete minimal cost, optimization with hydraulic constraints. A minimal cost algorithm is proposed, including hydraulic and water quality constraints. The suggested algorithm raises the concentration of the residual chlorine in the network by optimally decreasing the operational volume of the water tanks, and by such increasing the pump switching frequency. The algorithm is demonstrated and compared to enumeration on a single pressure zone example network (1 water tank, 1 pumping unit), on a large example network (C-Town, 7 water tanks, 11 pumping units). The resulting pump schedule is not a global minimum when compared to the best enumeration result on a single pressure zone. However, the algorithm may serve, especially in large water systems, as a quick and feasible answer to system operators, regarding the water volume to maintain in the different tanks to provide minimal chlorine service concentration at near minimal cost.

Modern water distribution systems (WDSs) largely depend on computer networks and industrial control systems for monitoring and operational purposes. Although the adoption of these cyber-physical components has improved the reliability and quality of service, such progressive computerization may render WDSs vulnerable to cyber and cyber-physical attacks. The spectrum of potential threats is very broad, with several attacks able to cause the disclosure of critical information or service disruption at different levels - a water supply interruption, for instance. These attacks usually target the supervisory control and data acquisition (SCADA) system - i.e., the centralized computer system supervising the whole infrastructure - or the programmable logic controllers (PLCs) that locally operate pumps and valves. In this work, we introduce an EPANET-based toolbox that allows simulating the effects of cyber-physical attacks on a WDS. Plausible attack scenarios to network SCADA and PLCs are implemented in EPANET to simulate the response of a large WDS and assess how it diverges from normal operating conditions.

In this work, we discuss the use of EPANET to simulate the effects of malicious cyber-physical attacks on water distribution systems. EPANET-a standard numerical modeling environment developed by the US Environmental Protection Agency-models hydraulic and water-quality behavior of pressurized pipe networks. EPANET promises to be well suited to show the effects of direct attacks on hydraulic actuators, such as pumps, or the effects of attacks on sensors. Using the C-Town benchmark network, we show that EPANET has some limitations when modeling these attacks, and we report the workarounds needed to overcome these limitations. In particular, we describe attacks that change the control strategies of pumps, and attacks that alter the tank water level reported by sensors.

Water distribution systems are vulnerable of being intentionally or accidentally contaminated. In the case of a contamination event the contaminated section of the water distribution system should be located, isolated, and cleaned before it can be returned to regular service. Depending on the specific contaminant, the cleaning process mainly includes flushing and disinfection. All of the disinfection methods (e.g., tablet, continuous, and slug) require that the disinfectants will have minimal contact time (T) in a predefined concentration (C) with the pipes and the system's apparatuses. The regulatory agencies, such as the Ministry of Health in Israel or the American Water Work Association, publishes procedures for the disinfection of water systems in which usually only a single water main is considered and no specific procedures are given for larger portions of the water system. This paper presents an optimal operation plan for the disinfection of water distribution systems using the slug feed method while considering the locations where the disinfectants should be injected into the network, injection times, flow rates, and drainage locations. The method is a two stage GA-EPANET procedure. It is demonstrated on a small real-world network section.

Event detection is one of the current most challenging topics in water distribution systems analysis: how regular on-line hydraulic (e.g., pressure, flow) and water quality (e.g., pH, residual chlorine, turbidity) measurements at different network locations can be efficiently utilized to detect water quality contamination events. This study describes an integrated event detection model which combines multiple sensor stations data with network hydraulics. To date event detection modelling is likely limited to single sensor station location and dataset. Single sensor station models are detached from network hydraulics insights and as a result might be significantly exposed to false positive alarms. This work is aimed at decreasing this limitation through integrating local and spatial hydraulic data understanding into an event detection model. The spatial analysis complements the local event detection effort through discovering events with lower signatures by exploring the sensors mutual hydraulic influences. The unique contribution of this study is in incorporating hydraulic simulation information into the overall event detection process of spatially distributed sensors. The methodology is demonstrated on two example applications using base runs and sensitivity analyses. Results show a clear advantage of the suggested model over single-sensor event detection schemes.

This paper presents a short history of water resources systems analysis from its beginnings in the Harvard Water Program, through its continuing evolution toward a general field of water resources systems science. Current systems analysis practice is widespread and addresses the most challenging water issues of our times, including water scarcity and drought, climate change, providing water for food and energy production, decision making amid competing objectives, and bringing economic incentives to bear on water use. The emergence of public recognition and concern for the state of water resources provides an opportune moment for the field to reorient to meet the complex, interdependent, interdisciplinary, and global nature of today's water challenges. At present, water resources systems analysis is limited by low scientific and academic visibility relative to its influence in practice and bridled by localized findings that are difficult to generalize. The evident success of water resource systems analysis in practice (which is set out in this paper) needs in future to be strengthened by substantiating the field as the science of water resources that seeks to predict the water resources variables and outcomes that are important to governments, industries, and the public the world over. Doing so promotes the scientific credibility of the field, provides understanding of the state of water resources and furnishes the basis for predicting the impacts of our water choices.

Effective decision support and model predictive control of real-time environmental systems require that evolutionary algorithms operate more efficiently. A suite of model predictive control (MPC) genetic algorithms are developed and tested offline to explore their value for reducing combined sewer overflow (CSO) volumes during real-time use in a deep-tunnel sewer system. MPC approaches include the micro-GA, the probability-based compact GA, and domain-specific GA methods that reduce the number of decision variable values analyzed within the sewer hydraulic model, thus reducing algorithm search space. Minimum fitness and constraint values achieved by all GA approaches, as well as computational times required to reach the minimum values, are compared to large population sizes with long convergence times. Optimization results for a subset of the Chicago combined sewer system indicate that genetic algorithm variations with a coarse decision variable representation, eventually transitioning to the entire range of decision variable values, are best suited to address the CSO control problem. Although diversity-enhancing micro-GAs evaluate a larger search space and exhibit shorter convergence times, these representations do not reach minimum fitness and constraint values. The domain-specific GAs prove to be the most efficient for this case study. Further MPC algorithm developments are suggested to continue advancing computational performance of this important class of problems with dynamic strategies that evolve as the external constraint conditions change.

Despite their potential catastrophic impact, transients are often ignored or presented ad hoc when designing water distribution systems. To address this problem, we introduce a new piece-wise function fitting model that is integrated with mixed integer programming to optimally place and size surge tanks for transient control. The key features of the algorithm are a model-driven discretization of the search space, a linear approximation nonsmooth system response surface to transients, and a mixed integer linear programming optimization. Results indicate that high quality solutions can be obtained within a reasonable number of function evaluations and demonstrate the computational effectiveness of the approach through two case studies. The work investigates one type of surge control devices (closed surge tank) for a specified set of transient events. The performance of the algorithm relies on the assumption that there exists a smooth relationship between the objective function and tank size. Results indicate the potential of the approach for the optimal surge control design in water systems. Key Points: Optimal closed surge tanks design is formulated using mathematical programming Discretization of the solution space is followed by approximation and MILP The algorithm is much more effective than crude simulation-based optimization

The problem of contamination event detection in water distribution systems has become one of the most challenging research topics in water distribution systems analysis. Current attempts for event detection utilize a variety of approaches including statistical, heuristics, machine learning, and optimization methods. Several existing event detection systems share a common feature in which alarms are obtained separately for each of the water quality indicators. Unifying those single alarms from different indicators is usually performed by means of simple heuristics. A salient feature of the current developed approach is using a statistically oriented model for discrete choice prediction which is estimated using the maximum likelihood method for integrating the single alarms. The discrete choice model is jointly calibrated with other components of the event detection system framework in a training data set using genetic algorithms. The fusing process of each indicator probabilities, which is left out of focus in many existing event detection system models, is confirmed to be a crucial part of the system which could be modelled by exploiting a discrete choice model for improving its performance. The developed methodology is tested on real water quality data, showing improved performances in decreasing the number of false positive alarms and in its ability to detect events with higher probabilities, compared to previous studies.

Cyanobacteria blooming in surface waters have become a major concern worldwide, as they are unsightly, and cause a variety of toxins, undesirable tastes, and odors. Approaches of mathematical process-based (deterministic), statistically based, rule-based (heuristic), and artificial neural networks have been the subject of extensive research for cyanobacteria forecasting. This study suggests a new framework of linking an evolutionary computational method (a genetic algorithm) with a data driven modeling engine (model trees) for external loading, physical, chemical, and biological parameters selection, all coupled with their associated time lags as decision variables for cyanobacteria prediction in surface waters. The methodology is demonstrated through trial runs and sensitivity analyses on Lake Kinneret (the Sea of Galilee), Israel. Model trials produced good matching as depicted through the results correlation coefficient on verification data sets. Temperature was reconfirmed as a predominant parameter for cyanobacteria prediction. Model optimal input variables and forecast horizons differed in various solutions. Those in turn raised the problem of best variables selection, pointing towards the need of a multiobjective optimization model in future extensions of the proposed methodology.

Placement of water quality sensors in a water distribution system is a common approach for minimizing contamination intrusion risks. This study incorporates detailed chemistry of organophosphate contaminations into the problem of sensor placement and links quantitative measures of the affected population as a result of such intrusions. The suggested methodology utilizes the stoichiometry and kinetics of the reactions between organophosphate contaminants and free chlorine for predicting the number of affected consumers. This is accomplished through linking a multi-species water quality model and a statistical dose-response model. Three organophosphates (chlorpyrifos, malathion, and parathion) are tested as possible contaminants. Their corresponding by-products were modeled and accounted for in the affected consumers impact calculations. The methodology incorporates a series of randomly generated intrusion events linked to a genetic algorithm for minimizing the contaminants impact through a sensors system. Three example applications are explored for demonstrating the model capabilities through base runs and sensitivity analyses.

Water distribution systems are liable to be contaminated. Depending on the nature of the contamination the cleaning process may include disinfection. The common requirement for disinfection is that the disinfectants will have a minimal contact time and a predefined minimum concentration with the pipe. The regulations consider disinfection of a single main but no specific procedures are given for larger portions of the network. This paper presents a multi-objective optimal operation plan for disinfection of water systems. The objective functions are to minimize the disinfection time and minimize the disinfectant quantities used while keeping the required regulations.

This paper explores two applied classification models alerting for contamination events in water distribution systems. The models perform multivariate analysis of water quality online measurements for event detection. The developed models comprise an outlier detection algorithm and a following sequence analysis for the classification of events. The first model is an unsupervised minimum volume ellipsoid (MVE), which utilizes only normal operation measurements but requires calibration. The second is a supervised weighted support vector machine, which utilizes event examples and performs data-driven optimized calibration. The models were trained and tested on real water utility data with randomly simulated events that were superimposed on the original database. The models showed high accuracy and detection ability compared to previous studies. All in all, the MVE model achieved preferable results.

Delivery of safe drinking water to consumers is a vital infrastructure of all populations. Any large water distribution system is inherently prone to fault from intentional or accidental contamination, placing large populations at risk. Monitoring these systems through a wireless network of stationary sensors (WSN) has shown to be an effective method to protect a water supply. Recent technological advancements allow for the implementation of a high resolution mobile wireless sensor network (MWSN); where sensors function within the water flowing through municipal pipes to measure water quality parameters and to transmit data to fixed ground transceivers. With mobile sensor prototypes being developed and tested, a MWSN is likely to be physically deployed in the near future. Previous work has shown an ideal MWSN to increase water security system performance. Accounting for uncertainties in: data collection, data transmission to fixed transceivers and sensor lifetimes will provide beneficial insight to the realistic performance of a MWSN. The non-ideal operation of mobile sensors is simulated and applied to sample municipal networks using EPANET and genetic algorithms to optimize the deployment of multiple mobile sensors, and quantify operational sensitivity. Results show a MWSN used for protection of public water supply to be highly sensitive to battery life and receiver network coverage, while the interval between measurements shows little affect to MWSN performance.

Certain soluble heavy metals are known to accumulate in the human body, resulting in (inter alia) toxicity to the kidney, liver, lungs, brain, heart and central nervous system. Water quality sensors can monitor small changes in water quality properties such as pH, TOC, turbidity, temperature, free chlorine concentration, and alkalinity. Heavy metals neither react with free chlorine nor consist of organic carbon; therefore, unless the solubility threshold is surpassed, the contaminant presence is distinguishable only by a change in the pH value. This characteristic makes the detection of heavy metal contamination events relatively tricky. In this work, a detailed aquatic chemistry multi-species model was developed within EPANET-MSX for the purpose of simulating the changes in water quality induced by cadmium contamination events. The model was applied on an example application network and the possible effects of various contamination events were explored.

Intrusion of contaminants into a water distribution system (WDS), deliberately or accidentally, is a potential risk facing water authorities and utilities. Deployment of methodologies for water quality sensor placements in water distribution systems is an approach for mitigation of such risks. This study incorporates a methodology for optimal placement of water quality sensors, through including contaminants detailed chemistry reactions. This is performed through using multispecies water quality model (EPANET-MSX) coupled with a statistical dose-response model. At the first stage, a series of contamination events are simulated, and the number of incident consumers' are evaluated. For each contamination event three decision variables are selected: injection location, contaminant type, and injection time. At the second stage, a genetic algorithm (GA) is invoked for selecting the optimal sensor placements through minimizing the average expected incidents. The method is demonstrated on an example application and two methods for events generation are compared and discussed.

The EPA's Water Quality Event Detection Challenge evaluated five event detection systems (EDS) with real world water quality monitoring data. The findings are that, on average, there are about one false positive alert per day per monitoring station. Other publications supports these findings. A real world municipal water distribution system (WDS) may consist of tens of monitoring stations resulting in a large number of false positive alerts thus making the system wide EDS unpractical from the WDS operator point of view. This paper presents a new approach for reducing false positive alerts by using alerts raised from more than one monitoring station at different times. First, the methodology uses the reversed hydraulic simulation method which allows to identify the possible time-locations pairs where contamination injection could explain each monitor's. Then, using the super-position method, the time-location pairs where contamination injection could explain all monitors alerts are found. In the next step a tracer injection simulation is performed for each time-location pair in order to validate that the tracer should not have triggered an alert in a non-alerting monitoring station or did not arrive before the alert for an alerting monitoring station. If such time-location pairs are found the alerts are suspected to be false positive. The method is demonstrated on a small water distribution system model.

Current attempts for event detection utilize a variety of approaches ranging from statistical models, to heuristics, machine learning and optimization methods. In this study, we combine the different approaches to build a high performance event detection system. A common feature with most developed event detection systems (EDSs) is that alarms are obtained as per water quality indicators. Unifying the single alarms from the different indicators was considered by means of simple heuristics. A salient feature of the developed approach is using a statistically oriented model for discrete choice prediction which is estimated using a maximum likelihood method. Specifically, each of the water quality indicators is considered as an expert with its own opinion regarding contamination event probability. A linear utility function which uses the experts' probabilities as input variables is then used to inform the discrete choice model on the selection between event and non-event decisions. The discrete choice model is jointly calibrated with other components of the EDS framework in a training data using genetic algorithms. As such, the proposed framework saves us from the need for heuristics to unify single indicator probabilities which was proven to be a crucial part in other EDSs.

A water distribution system is a complex assembly of hydraulic control elements connected together to convey quantities of water from sources to consumers. The typical high number of constraints and decision variables, the nonlinearity, and the non-smoothness of the head—flow—water quality governing equations are inherent to water supply systems planning and management problems. Traditional methods for solving water distribution systems management problems, such as the least cost design and operation problem, utilized linear/ nonlinear optimization schemes which were limited by the system size, the number of constraints, and the number of loading conditions. More recent methodologies employ heuristic optimization techniques, such as genetic algorithms or ant colony optimization as stand alone or hybrid data driven—heuristic schemes. This book chapter reviews some of the more traditional water distribution systems problem algorithms and solution methodologies. It is comprised of sub sections on least cost and multi-objective optimal design of water networks, reliability incorporation in water supply systems design, optimal operation of water networks, water quality analysis inclusion in distribution systems, water networks security related topics, and a look into the future.

This study features a contamination warning system utilizing on-line water quality data collected from multiple sensors spread in the network, into an integrated event detection model. The model is comprised of an outlier detection engine, conducted for each of the sensors' data streams separately, which is followed by a spatial event classification procedure. The classification routine incorporates the data analysis of all sensors, and the network hydraulics for a unified spatial warning system. When using multiple sensors information the challenge to distinguish between normal behavior of the parameters, and changes triggered by contaminants intrusions becomes more complex. Previous studies included information for water quality event detection from only one sensor. This study extends event detection modeling to incorporate multiple sensors and water quantity and hydraulic extend period simulation information. The methodology is demonstrated on an example application.

The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.