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Water and energy are essential resources for human life, and carbon emissions (CEs) occur in tandem with their use. Thus, water, energy, and carbon are closely inter-related. Approximately 4% of the global energy is used in urban water sectors (UWSs), which encompass various processes such as water intake, treatment, and distribution and wastewater collection and treatment, all of which consume significant energy and emit CO2. Several countries are actively working toward achieving carbon neutrality by 2050–2060. Therefore, increasing energy efficiency and reducing CEs through comprehensive evaluations of UWSs is essential. This study aimed to quantify energy consumption and CEs in UWSs and proposed a methodology for analyzing water–energy–carbon (WEC) links at the city level. By applying it to Incheon Metropolitan City (IMC), we first identified the UWSs and established a WEC database. Based on this database, the WEC consumption and emissions were analyzed by process or administrative district, and visualizations using Sankey diagrams and Geographic Information System Mapping were created to enhance their understandability. In 2021, the UWSs in IMC consumed 308,496,107 kWh of energy, representing 32.7% of the public electricity consumption of IMC, with an average energy intensity of 0.46 and 0.38 kWh/m³ for water supply systems (WSSs) and sewerage systems (SSs), respectively. Their carbon emissions totaled 315,765,358 kg CO2, accounting for 2.7% of IMC's total carbon emissions, with an average carbon intensity of 0.21 and 0.58 kg CO2/m³ for WSSs and SSs, respectively. The proposed methodology was used to comprehensively evaluate WEC consumption and emissions in IMC. It is expected to enable relevant stakeholders to develop measures, such as water reuse and increasing renewable energy usage in water treatment and wastewater treatment plants, to build sustainable UWSs. [ABSTRACT FROM AUTHOR]

Capturing the criticality of a water distribution network (WDN) is difficult because of its many constituent factors. In terms of operation, the arrangement of demand nodes and how they connect have a significant influence. This study aims to integrate hydraulic and topologic aspects into a single criticality measure by adapting the structural hole influence matrix concept. This method applies the nodal demand to the corresponding pipes to construct a weighted network. The matrix stores each node's local and global connection information, and the criticality value is then assigned based on the adjacency information. The criticality value can reveal the locations in terms of nodes or pipes that are vital for maintaining a network's level of service. By analyzing pipe-failure scenarios, the criticality value can be related to the loss of performance. Assessing the nodal criticality change behavior under an increased stress scenario can help uncover the impacted areas. The metric for district metered area (DMA) creation demonstrates its potential as a weighting to be considered. This unified criticality metric enables the evaluation of nodes and pipes in a WDN, thereby enabling resilient and sustainable development planning. [ABSTRACT FROM AUTHOR]

Chlorine decay over time and distance travelled poses challenges in maintaining consistent chlorine levels from treatment plants to demand nodes in water distribution networks (WDNs). Many studies have focused on optimizing chlorine booster systems and addressing dosage and location. This study proposes a chlorine injection optimization model for maintaining spatial and temporal chlorine residuals within an acceptable range. First, the approach involves identifying potential pathways from the source to demand nodes using a breadth-first search (BFS) algorithm. Subsequently, the required chlorine injection to maintain a 0.2 mg/L residual chlorine level at demand nodes is estimated based on water age. Finally, a single-objective genetic algorithm optimizes the chlorine injection schedule at the source. The results demonstrated that chlorine estimation based on water age exhibited promising results with an average error below 10%. In addition, the four-interval injection scheme performed well in adapting to changing demand patterns, making the method robust to varying demand patterns. Moreover, the model could accommodate fluctuating water temperature conditions according to operating seasons. This study provides valuable insights into effectively managing chlorine levels and operations of WDNs, and paves the way for using water age for chlorine estimation. [ABSTRACT FROM AUTHOR]

A model that computes the per-unit process energy consumption, energy intensity, CO2 emission, and CO2 intensity of water treatment plants is developed. This model is used to estimate the total energy consumption of six water treatment plants in Seoul Metropolitan City (SMC), which is comprised 80–85% for finished water pumping, 6–10% for ozone disinfection, 2–4% for rapid mixing, and 1–3% for non-process loads. The model results are validated against actual data for 2020 and 2021. The net energy consumption considering renewable energy production and use is then calculated, and the corresponding level of CO2 emissions is predicted. Four scenarios based on the projected water requirements for the year 2045 were evaluated as follows: increased energy efficiency in finished water pumping (Scenario 1), increased renewable energy production in water treatment plants (Scenario 2), increased energy efficiency in raw water pumping (Scenario 3), and reduced water supply per capita (Scenario 4). Compared to a baseline do-nothing scenario (Scenario 0), the net energy consumption is reduced by 3.57%, 2.61%, 3.42%, and 4.67% for Scenarios 1–4, respectively. Scenario 4, which is a water-driven approach, is best for reducing CO2 emissions, while Scenario 1 and 3, which are energy-driven approaches, are more effective at reducing CO2 intensity. [ABSTRACT FROM AUTHOR]

Enhancing the generalization capability of time-series models for streamflow prediction using dimensionality reduction (DR) techniques remains a major challenge in water resources management (WRM). In this study, we investigated eight DR techniques and their effectiveness in mitigating the curse of dimensionality, which hinders the performance of machine learning (ML) algorithms in the field of WRM. Our study delves into the most non-linear unsupervised representative DR techniques, including principal component analysis (PCA), kernel PCA (KPCA), multi-dimensional scaling (MDS), isometric mapping (ISOMAP), locally linear embedding (LLE), t-distributed stochastic neighbor embedding (t-SNE), Laplacian eigenmaps (LE), and autoencoder (AE), examining their effectiveness in multi-step ahead (MSA) streamflow prediction. In this study, we conducted a conceptual comparison of these techniques. Subsequently, we focused on their performance in four different case studies in the USA. Moreover, we assessed the quality of the transformed feature spaces in terms of the MSA streamflow prediction improvement. Through our investigation, we gained valuable insights into the performance of different DR techniques within linear/dense/convolutional neural network (CNN)/long short-term memory neural network (LSTM) and autoregressive LSTM (AR-LSTM) architectures. This study contributes to a deeper understanding of suitable feature extraction techniques for enhancing the capabilities of the LSTM model in tackling high-dimensional datasets in the realm of WRM. [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are some of the most energy-intensive urban infrastructures and thus require efficient energy management. As an essential public infrastructure, a WDS plays an integral role in meeting the water needs of its users at service pressure. Hence, the service level should also be considered when reducing the energy consumption of the WDS. Therefore, to evaluate both energy management and service level, this study proposes efficient returned pressure (ERP) as a metric to optimize the WDS in both aspects by comparing the service pressure to the required energy intensity. During its operating cycle, the ERP considers the pressure and required energy intensity of the demand junctions resulting from the connection of various WDS elements. Using ERP as an optimization objective against the cost for three cases of different active network element configurations, it was discovered that ERP successfully identified solutions that could maximize service pressure while maintaining a minimum required energy intensity. Using ERP provided more effective solutions in terms of cost, greenhouse gas emissions, and network pressure uniformity compared to a conventional index such as the modified resilience index. Overall, the ERP proves to be a feasible optimization parameter when pressure and energy usage is of concern. [ABSTRACT FROM AUTHOR]

In accordance with the rapid proliferation of machine learning (ML) and data management, ML applications have evolved to encompass all engineering disciplines. Owing to the importance of the world's water supply throughout the rest of this century, much research has been concentrated on the application of ML strategies to integrated water resources management (WRM). Thus, a thorough and well-organized review of that research is required. To accommodate the underlying knowledge and interests of both artificial intelligence (AI) and the unresolved issues of ML in WRM, this overview divides the core fundamentals, major applications, and ongoing issues into two sections. First, the basic applications of ML are categorized into three main groups, prediction, clustering, and reinforcement learning. Moreover, the literature is organized in each field according to new perspectives, and research patterns are indicated so attention can be directed toward where the field is headed. In the second part, the less investigated field of WRM is addressed to provide grounds for future studies. The widespread applications of ML tools are projected to accelerate the formation of sustainable WRM plans over the next decade. [ABSTRACT FROM AUTHOR]

In recent decades, natural calamities such as drought and flood have caused widespread economic and social damage. Climate change and rapid urbanization contribute to the occurrence of natural disasters. In addition, their destructive impact has been altered, posing significant challenges to the efficiency, equity, and sustainability of water resources allocation and management. Uncertainty estimation in hydrology is essential for water resources management. By quantifying the associated uncertainty of reliable hydrological forecasting, an efficient water resources management plan is obtained. Moreover, reliable forecasting provides significant future information to assist risk assessment. Currently, the majority of hydrological forecasts utilize deterministic approaches. Nevertheless, deterministic forecasting models cannot account for the intrinsic uncertainty of forecasted values. Using the Bayesian deep learning approach, this study developed a probabilistic forecasting model that covers the pertinent subproblem of univariate time series models for multi-step ahead daily streamflow forecasting to quantify epistemic and aleatory uncertainty. The new model implements Bayesian sampling in the Long short-term memory (LSTM) neural network by using variational inference to approximate the posterior distribution. The proposed method is verified with three case studies in the USA and three forecasting horizons. LSTM as a point forecasting neural network model and three probabilistic forecasting models, such as LSTM-BNN, BNN, and LSTM with Monte Carlo (MC) dropout (LSTM-MC), were applied for comparison with the proposed model. The results show that the proposed Bayesian long short-term memory (BLSTM) outperforms the other models in terms of forecasting reliability, sharpness, and overall performance. The results reveal that all probabilistic forecasting models outperformed the deterministic model with a lower RMSE value. Furthermore, the uncertainty estimation results show that BLSTM can handle data with higher variation and peak, particularly for long-term multi-step ahead streamflow forecasting, compared to other models. [ABSTRACT FROM AUTHOR]

Water distribution network (WDN) is a human-centered infrastructure that is indispensable for modern cities worldwide. In addition to optimizing the operation and management (O&M) of WDNs under the current state, water utilities should be able to manage uncertain and risk conditions for improving their O&M efficiency. Although the disintegration of large WDNs into permanent district metered areas (DMAs) is an O&M innovation based on water leakage monitoring and pressure management, its network redundancy and reliability diminish under anomalous conditions. Therefore, this study proposed a design and operation procedure to obtain optimal, self-adaptive DMA configurations for various plausible abnormal scenarios. The proposed method is based on multiscenario simulation and optimization, comprising two phases: (1) design of optimal DMA layout for each scenario using the pressure uniformity index to optimize the placement of flow meters and gate valves, and (2) dynamic transformation of the base DMA configuration into an adaptive DMA layout adapting to abnormal conditions and optimization of the locations and statuses of the control valves. Moreover, we used a real-world WDN to demonstrate the effectiveness of the proposed approach, and the obtained results revealed the efficiency and appropriate performance of the adaptive DMA layouts for sustainable adaptation of WDNs under anomalous conditions. [ABSTRACT FROM AUTHOR]

In water distribution networks (WDNs), pipe failure is the most frequent type of equipment malfunction, resulting in water supply suspension. To determine the water suspension area and to properly plan restoration work, the accurate identification of isolated subsystems with respect to valve locations is critical. The water suspension area can be divided into intended and unintended isolations, depending on the valve layout. This technical note discusses the conventional method of searching the valve-controlled isolation areas and specifically examines the weakness of the unintended isolation (UI) search algorithm that uses a node-by-node adjacency matrix. In this study, we propose a new search algorithm using a link-by-link adjacency matrix to identify the UI segments. Demonstrations on branch and loop networks show that the new approach correctly identifies the UI in various operating conditions. The proposed method is expected to replace the conventional method and can be applied in various valve-controlled segment analyses, such as optimal valve placement, real-time valve control for dynamic WDN operation, and postaccident pipe recovery planning. [ABSTRACT FROM AUTHOR]

A water distribution network (WDN) is a critical infrastructure that must be maintained, ensuring a proper water supply to widespread customers. A WDN consists of various components, such as pipes, valves, pumps, and tanks, and these elements interact with each other to provide adequate system performance. If the elements fail due to internal or external interruptions, this may adversely impact water service to different degrees depending on the failed elements. To determine an appropriate maintenance priority, the critical elements need to be identified and mapped in the network. To identify and prioritize the critical elements (here, we focus on the pipes only) in the WDN, an element-based simulation approach is proposed, in which all the composing pipes of the WDN are reviewed one at a time. The element-based criticality is measured using several criticality indexes that are newly proposed in this study. The proposed criticality indexes are used to quantify the impacts of element failure to water service degradation. Here, four criticality indexes are developed: supply shortage (SS), economic value loss (EVL), pressure decline (PD), and water age degradation (WAD). Each of these indexes measures different aspects of the consequences, specifically social, economic, hydraulic, and water quality, respectively. The separate values of the indexes from all pipes in a network are then combined into a singular criticality value for assessment. For demonstration, the proposed approach is applied to four real WDNs to identify and prioritize the critical pipes. The proposed element-based simulation approach can be used to identify the critical components and setup maintenance scheduling of WDNs for preparedness of failure events. [ABSTRACT FROM AUTHOR]

Metaheuristic Optimization Algorithms (MOA) have been widely employed for optimal designs of Water Distribution System (WDS). Generally, the size, capacity, and location of WDS components (e.g., pipes, pumps, and tanks) are determined in the WDS design. Therefore, solutions with good fitness have engineeringly sound value of decision variables and share commonality. For example, a solution with transmission pipes bigger than distribution pipes is better with respect to fitness than that with the opposite case in the pipe sizing problem. However, few efforts have been made to consider good fitness rules in the optimization. In this study, we develop a hybrid harmony search (HyHS) algorithm that combines Harmony Search (HS) and a rule induction algorithm. The proposed HyHS algorithm’s performance is compared with an improved GA, improved HS, and hybrid GA through optimizations of well-known benchmark functions and two WDS designs. The four algorithms are first applied to the optimization of the De Jong functions. Then, the least-cost and robustness-based design problems of New York tunnel system are solved using the four algorithms. The application results confirm that the proposed HyHS outperforms the other algorithms in terms of computational speed and effectiveness. [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are social infrastructures providing drinking water and must be capable of constant water supply while maintaining an appropriate water pressure. Hence, it is important to quantitatively evaluate the supply capacity of a WDS for design and operation purposes. As part of such efforts, several resilience indexes have been developed based on the energy flows within a network. Accurate estimation of a minimum required head at the demand node is critical for properly calculating the resilience indexes. This study proposes a novel approach of estimating the minimum required head, which considers the flow direction in pipes and the hydraulic gradient within a network. The proposed approach has been implemented and tested for six hypothetical grid-type networks that reflect various topographical characteristics. The application results revealed that the conventional resilience indexes are significantly affected by the nodal elevation and are generally overestimated due to incorrect calculation of the minimum required nodal head. [ABSTRACT FROM AUTHOR]

This study develops a real-time near-optimal pump scheduling model for water transmission networks. The model aims to minimize energy cost by means of optimal pump operation while meeting system demands and satisfying operational constraints. The real-time control is realized using Supervisory Control and Data Acquisition (SCADA) system, by which system monitoring data is collected and the optimal control decisions are sent to the pumping stations for implementation. The model is composed of three simulation modules, such as demand forecasting, hydraulic simulation, and optimization. Decision variables include pump on/off (binary code of 0/1) schedules for fixed-rate pumps for each time-step over pre-defined operation time horizon. Various operational constraints are embedded in the model reflecting real operational practices. The proposed optimization model is demonstrated using a real, medium-sized water transmission system in Seoul, South Korea and showed 19~27% energy cost savings compared to a conventional operation scheme. [ABSTRACT FROM AUTHOR]

Contamination events in water distribution networks (WDNs) begin with contaminant inception in the network. WDNs respond to events according to the detection, stopping service, and recovery phases. The recovery phase aims to remove hazardous substances by flushing them out so that the network can return to normal conditions. Flushing must be conducted efficiently and safely. The contaminated water is removed by allowing it to flow from outlet points in the network, which is enabled by displacing it with clean water from the source. Conventionally, a hydrant was used as the outlet point. Recent advancements in information and communication technology allow the use of electronic media to broadcast warnings and guidance rapidly. Water utilities can convey information to customers as part of the flushing scheme by notifying them to open and close their faucets at designated times. In this study, the viability of customer involvement in decontamination was examined. The proposed method was tested by evaluating its effectiveness in terms of the time and volume of water needed for decontamination, and the change in hydraulics to drain a fully contaminated district metered area (DMA). A comparable performance to hydrant flushing was found after testing in two actual DMA-sized WDNs. [ABSTRACT FROM AUTHOR]

This work presents a postoptimization methodology for refining the solutions found by adaptive search algorithms used in the design of large water distribution networks. The approach uses two heuristics to search for an optimal combination of pipes that, after a reduction of their diameters, will maximize cost savings while continuing to meet design constraints. Adaptive search methods are often used to design urban water distribution networks when the number of pipes in the network is insignificant. For complex, real-world networks, however, such methods are computationally demanding, and they have difficulty finding near-global optima. To identify a solution as close to the global optimum (and in which no pipe can be reduced without violating pressure constraint), requires a high-speed computer potentially running for a long time and also probably some good fortune. The postoptimization approach presented in this paper is shown to be an efficient complement to heuristic search algorithms used in the design of real-world networks. In a network created with the aid of a genetic algorithm, the proposed heuristics found that 4.37% of the pipes with a diameter greater than the minimum could be further reduced without causing hydraulic failure. [ABSTRACT FROM AUTHOR]

A life cycle analysis (LCA) was performed for six commonly used types of water and wastewater pipe materials: polyvinyl chloride (PVC), ductile iron, cast iron, high density polyethylene (HDPE), concrete, and reinforced concrete. The objectives were to (1) compare the six pipe materials in terms of global warming potential (GWP) through four LCA phases: pipe production, transport, installation, and use; (2) determine the primary source(s) of differences in LCA results; and (3) examine the effectiveness of currently used pipe size selection criteria when LCA GWP is considered. The results for unit lengths of discrete pipe sizes were used to generate functions relating GWP per kilometer of pipe to diameter and material selections. The LCA results were monetized using an emission penalty of ton of . For pipe diameters , GWP due to pipe manufacture, transport, and installation of ductile iron pipe was the largest among the six materials. At diameters , the GWP of PVC was highest. Concrete pipe resulted in the lowest GWP across the entire range of pipe sizes investigated. The GWP for pipe production, transport, and installation in a high-growth planning area in southeast Tucson, Arizona, was approximately one-tenth of the GWP derived from pipe network operation. The lifetime GWP from production, transport, and installation increased monotonically with pipe diameter for all materials analyzed, whereas, for a given flow, GWP from energy loss due to friction in flow simulations was inversely related to pipe diameter. The tradeoff suggests that there is an optimum diameter that minimizes lifetime GWP. However, optimum pipe sizes based on GWP were similar to pipe diameters selected based on economic cost alone, suggesting that LCA of water distribution and wastewater collection systems will not yield major changes in criteria for selection of pipe size. [ABSTRACT FROM AUTHOR]

In last three decades, the reliability of water distribution systems (WDSs) has become a major concern, not only for water utilities and academic research communities, but also for society as a whole. Notable research has been conducted on the reliability/availability of WDSs, however, little attention has been paid to quantify the impact of adaptive operations as a response to system failure to enhance the resiliency of WDSs. Here, resilience is defined in the general sense as the ability to recover from a failure to a satisfactory state. In practice, if a system fails to supply water with adequate pressure, the water utility would take action to respond. Adaptive actions include switching on additional pumps as a short-term remedy or maintaining a higher water level in storage tanks as a long-term strategy to satisfy system pressure when a system experiences abnormal conditions. Here, a Monte Carlo simulation based framework for the resilience analysis of WDSs is implemented, considering the impact of adaptive pump operations and isolation valve locations. The framework consists of four steps: (1) random event generation for nodal demand fluctuations and pipe breaks; (2) identification of isolated segments based on valve layout; (3) hydraulic simulation with regular and adaptive operations; and (4) identification of responses and the evaluation of system resilience/availability. The proposed methodology is applied to a mid-sized WDS and results show that adaptive pump operations improve system resilience with a moderate pumping cost increase. [ABSTRACT FROM AUTHOR]

Reclaimed wastewater is increasingly important to satisfaction of water-sustainability objectives in water-short municipalities throughout the United States and particularly in the Southwest. Water reclamation and reuse present new challenges for urban planners, who now tend to consider renewable freshwater and reclaimed wastewater as unique parts of a single water resources portfolio. Efficiency objectives in geographically dispersed communities lead planners to explore the relative merits of centralized versus decentralized wastewater-treatment capacity when new construction is required. However, the complexity of the planning landscape-in which existing water distribution and sewerage capacities; geographic factors; and uncertainty in growth projections, energy cost, and even the sustainability of existing freshwater supplies contribute to plan selection-suggests that decision support methods can usefully supplement engineering judgment to find a near-optimal level of decentralization in facilities planning. In this study, an existing decision support system (DSS) was modified to include costs attributable to infrastructure construction, operation, and maintenance for wastewater collection and transmission of both potable and reclaimed water at the regional (city or city subsection) level to aid water supply planning. The modified DSS was then applied to a study area in southeast Tucson, Arizona. Several scenarios are developed and compared on the basis of cost and energy consumption. A sensitivity analysis is provided. In general, increased peripheral demand, limited existing capacity, greater elevation differences, and lower discount rates favor decentralized design and construction. [ABSTRACT FROM AUTHOR]

Uncertainties facing water planners and designers include changes in demand resulting from community growth (temporal and spatial), per capita water use, public perceptions, and regulations. One of the most powerful and intuitive ways to represent these uncertainties in the planning stage is to use scenarios. A scenario represents a realization of the system's random parameters. Scenario-based analysis provides organizational flexibility by planning for multiple potential future scenarios, each of which may be, although is not necessarily, associated with a probability of occurrence. In this study, a scenario-based multiobjective optimization model for robust optimal design of regional-scale water and wastewater infrastructure is presented. The proposed methodology is demonstrated with an application to the planning of a decentralized water supply and reuse system for a new development area in metropolitan Tucson, where sustainable water supply is a significant issue. [ABSTRACT FROM AUTHOR]

Calibration is a process of comparing model results with field data and making the appropriate adjustments so that both results agree. Calibration methods can involve formal optimization methods or manual methods in which the modeler informally examines alternative model parameters. The development of a calibration framework typically involves the following: (1) definition of the model variables, coefficients, and equations; (2) selection of an objective function to measure the quality of the calibration; (3) selection of the set of data to be used for the calibration process; and (4) selection of an optimization/manual scheme for altering the coefficient values in the direction of reducing the objective function. Hydraulic calibration usually involves the modification of system demands, fine-tuning the roughness values of pipes, altering pump operation characteristics, and adjusting other model attributes that affect simulation results, in particular those that have significant uncertainty associated with their values. From the previous steps, it is clear that model calibration is neither unique nor a straightforward technical task. The success of a calibration process depends on the modeler's experience and intuition, as well as on the mathematical model and procedures adopted for the calibration process. This paper provides a summary of the Battle of the Water Calibration Networks (BWCN), the goal of which was to objectively compare the solutions of different approaches to the calibration of water distribution systems through application to a real water distribution system. Fourteen teams from academia, water utilities, and private consultants participated. The BWCN outcomes were presented and assessed at the 12th Water Distribution Systems Analysis conference in Tucson, Arizona, in September 2010. This manuscript summarizes the BWCN exercise and suggests future research directions for the calibration of water distribution systems. [ABSTRACT FROM AUTHOR]

For the past three decades, a number of studies have been dedicated to water-distribution system (WDS) optimal design using alternative optimization algorithms. Many of those, however, focused on the introduction and application of new optimization techniques. Application systems optimized in previous studies are generally limited to simple transmission networks, so-called benchmark systems, in which local distribution lines were mostly excluded. Efforts seeking logical approaches to solve complex problems with large number of decisions are lacking. In this paper, logical and efficient approaches that could be utilized to optimize real-life scale WDS by the aid of existing optimization techniques are presented. This study aimed two main objectives: first, the effect of local distribution lines in final system design is investigated, and second, a heuristic to improve the efficiency of meta-heuristic search methods is proposed. Applications to real WDS demonstrate that (1) by integrating the transmission and distribution scales in optimization model, oversizing the transmission system could be avoided and the capacity of local distribution pipes could be appropriately evaluated, and (2) a proposed heuristic is logical and improves optimization performances, and is easily transferrable to any type of random search algorithms. Other issues related to solving the design problem facing engineers are raised and research directions are proposed. [ABSTRACT FROM AUTHOR]

Rapid urban growth has stressed limited supplies of high-quality water sources and water shortage has become a major issue in worldwide. Today, sustainable water supply to meet growing demand is a significant concern for water utilities. Developing additional high-quality sources is not necessarily available; it may be costly to convey water from sources that are usually located far from users or there may be legal battles over water rights. A well-proven source that does not have significant conveyance costs and that does not pose water right conflicts is wastewater reclamation for nonpotable uses. A few studies have been completed for the design of reclaimed water distribution through a parallel pipe system. In earlier works, however, attention has mainly focused on the recycling of effluent for large users, such as golf courses, parks, and schools with the objective of minimizing economic cost. As reclaimed water takes on a larger role in many communities' water supply, the questions of how and what water to deliver to consumers must be addressed. To that end, this study presents a multi-objective optimization algorithm for designing dual-water distribution systems that recyce effluents for local residential nonpotable uses. Triple-bottom-line objectives are posed, as well as economic costs for pipe and pump installation and operation, environmental costs represented by greenhouse gas production, and system reliability against mechanical failure representing social cost. Under the premise that the systems will be constructed in a new community, thorough comparisons are made between a conventional single system and three alternative dual systems. The resulting problems are mixed integer problems and solved using a genetic algorithm (GA) linked to a hydraulic simulation model. Applications begin to demonstrate water deliver trade-offs and can guide policy decisions on appropriate system types taking a fuller picture of system costs and effects. [ABSTRACT FROM AUTHOR]

To provide more accurate estimates and account for associated uncertainties, a parameter estimation methodology for water distribution systems (WDSs) that combines demand and parameter estimation processes is proposed. A two-step sequential method for dual estimation of demand and roughness coefficient is presented based on a weighted least-squares scheme using field measurements of pipe flow rates and nodal pressure heads under multiple demand conditions. The uncertainties in the estimated variables and resulting nodal pressure predictions are quantified in terms of confidence limits using the first-order second moment method. The algorithm is applied to two network systems including a midsized real WDS. The two-step sequential model provides accurate and precise estimates while joint estimation provides poor estimates. [ABSTRACT FROM AUTHOR]

Demand estimation has been solved by using a weighted least-squares (WLS) estimator incorporating field measurements with system simulation model. WLS estimator results are sensitive to spurious measurements caused by supervisory control and data acquisition malfunctions. Estimates using the contaminated measurements are not reliable and bad data should be filtered prior to demand estimation. This study presents a series of statistical methods to detect bad data, identify their locations, and correct the data values. The proposed methods are based on a linear measurement model that linearly relates state variables (nodal demands) to the field measurements (pipe flow rates). Application to a simple hypothetical network using synthetically generated data shows that the method can be successfully used as a preprocessing for single and multiple noninteracting bad data for reliable demand estimation. [ABSTRACT FROM AUTHOR]

Historically, a water distribution system’s (WDS) hydraulic performance has been the primary operational concern. Over the past two decades, however, more attention has been paid to water quality behavior in WDS and today, water quality level is an equally important issue for many water utilities. In most cases, maintaining disinfectant levels is usually of interest to avoid the bacteria regrowth and to protect against the potential cross-contamination events. However, disinfectants, such as chlorine, decay over time and produce potentially harmful disinfectant by-products when they react with organic material in the water. Therefore, maintaining a minimum chlorine residual requirement throughout the WDS is a complex but important task. When online booster disinfection is combined with source disinfection, it has been shown that the total chlorine dosage can be reduced while maintaining minimum chlorine residuals across the system. Here, optimal valve operation has been combined with booster disinfection to improve the system water quality. Valves can be operated to alter the flow distribution in the network; prevent the isolation of water; and direct disinfectant laden water to locations where it is needed. A real-time optimal valve operation and booster disinfection problem is formulated as a single objective optimization model. The objective is to minimize chlorine injection mass at sources or to minimize excessive chlorine concentrations at withdrawal points while maintaining minimum chlorine concentrations and pressures throughout the system. The problem is solved using a genetic algorithm (GA). The application to a medium-sized WDS shows that optimal operation of existing valves combined with booster disinfection can improve water quality while requiring lower chlorine doses and resulting in little significant pressure reduction. Also, real-time operations can adapt to the temporal and spatial variations of system demands. [ABSTRACT FROM AUTHOR]

Real-time state estimates (SEs) of nodal demands in a water distribution system (WDS) can be developed using data from a supervisory control and data acquisition (SCADA) system. These estimates provide information for improved operations and customer service in terms of energy consumption and water quality. The SE results in a WDS are significantly affected by measurement characteristics, i.e., meter types, numbers, and topological distributions. The number and type of meters are generally selected prior to a SCADA layout. Thus, selecting measurement locations is critical. The aim of this study is to develop a methodology that optimally locates field measurement sites and leads to more reliable SEs. An optimal meter placement (OMP) problem is posed as a multiobjective optimization form. Three distinctive objectives are formulated: (1) minimization of nodal demand estimation uncertainty; (2) minimization of nodal pressure prediction uncertainty; and (3) minimization of absolute error between demand estimates and their expected values. Objectives (1) and (2) represent the model precisions while Objective (3) describes the model accuracy. The OMP is solved using a multiobjective genetic algorithm (MOGA) based on Pareto-optimal solutions. The trade-off between model precision and accuracy is clearly observed in two case studies and it is recommended to use both criteria as objectives. It is also concluded that the proposed objectives are more appropriate for OMP purposes compared to calibration sampling design studies in which minimization of metering costs (i.e., number of meters) is used as one of the multiple objectives. The MOGA saves computational effort while providing optimal Pareto solutions compared to full enumeration for a small hypothetical network. For real networks, GA solutions, although not guaranteed to be globally optimal, are improvements over those obtained using less robust methods or designers’ experienced judgment. [ABSTRACT FROM AUTHOR]

In our intelligent society, water resources are being managed using vast amounts of hydrological data collected through telemetric devices. Recently, advanced data quality control technologies for data refinement based on hydrological observation history, such as big data and artificial intelligence, have been studied. However, these are impractical due to insufficient verification and implementation periods. In this study, a process to accurately identify missing and false-reading data was developed to efficiently validate hydrological data by combining various conventional validation methods. Here, false-reading data were reclassified into suspected and confirmed groups by combining the results of individual validation methods. Furthermore, an integrated quality control process that links data validation and reconstruction was developed. In particular, an iterative quality control feedback process was proposed to achieve highly reliable data quality, which was applied to precipitation and water level stations in the Daecheong Dam Basin, South Korea. The case study revealed that the proposed approach can improve the quality control procedure of hydrological database and possibly be implemented in practice. [ABSTRACT FROM AUTHOR]

Contamination in water distribution networks (WDNs) can occur at any time and location. One protection measure in WDNs is the placement of water quality sensors (WQSs) to detect contamination and provide information for locating the potential contamination source. The placement of WQSs in WDNs must be optimally planned. Therefore, a robust sensor-placement strategy (SPS) is vital. The SPS should have clear objectives regarding what needs to be achieved by the sensor configuration. Here, the objectives of the SPS were set to cover the contamination event stages of detection, consumption, and source localization. As contamination events occur in any form of intrusion, at any location and time, the objectives had to be tested against many possible scenarios, and they needed to reach a fair value considering all scenarios. In this study, the particle swarm optimization (PSO) algorithm was selected as the optimizer. The SPS was further reinforced using a databasing method to improve its computational efficiency. The performance of the proposed method was examined by comparing it with a benchmark SPS example and applying it to DMA-sized, real WDNs. The proposed optimization approach improved the overall fitness of the configuration by 23.1% and showed a stable placement behavior with the increase in sensors. [ABSTRACT FROM AUTHOR]

Water pipe leaks due to seismic damage are more difficult to detect than bursts, and such leaks, if not repaired in a timely manner, can eventually reduce supply pressure and generate both pollutant penetration risks and economic losses. Therefore, leaks must be promptly identified, and damaged pipes must be replaced or repaired. Leak-detection using equipment in the field is accurate; however, it is a considerably labor-intensive process that necessitates expensive equipment. Therefore, indirect leak detection methods applicable before fieldwork are necessary. In this study, a computer-based, multiple-leak-detection model is developed. The proposed technique uses observational data, such as the pressure and flow rate, in conjunction with an optimization method and hydraulic analysis simulations, to improve detection efficiency (DE) for multiple leaks in the field. A novel approach is proposed, i.e., use of a cascade and iteration search algorithms to effectively detect multiple leaks (with the unknown locations, quantities, and sizes encountered in real-world situations) due to large-scale disasters, such as earthquakes. This method is verified through application to small block-scale water distribution networks (WDNs), and the DE is analyzed. The proposed detection model can be used for efficient leak detection and the repair of WDNs following earthquakes. [ABSTRACT FROM AUTHOR]

Rational water resource management is used to ensure a stable supply of water by predicting the supply of and demand for future water resources. However, rational water allocation will become more difficult in the future owing to the effects of climate change, causing water shortages and disputes. In this study, an advanced hydro-economic water allocation and management model (WAMM) was introduced by improving the optimization scheme employed in conventional models and incorporating the economic value of water. By relying upon economic valuation, the WAMM can support water allocation efforts that focus not only on the stability but also on the economic benefits of water supply. The water supply risk was evaluated following the different objective functions and optimization methods provided by the WAMM using a case study of the Namhan River basin in South Korea under a climate change scenario over the next 30 years. The water shortages and associated economic damage were compared, and the superior ability of WAMM to mitigate future water shortages using economic valuation and full-step linear programming (FSLP) optimization was demonstrated. It is expected that the WAMM can be applied to help resolve water shortages and disputes among river basin units under severe drought conditions. [ABSTRACT FROM AUTHOR]

Operation and management of a water distribution network (WDN) by district metered areas (DMAs) bring many benefits for water utilities, particularly regarding water loss control and pressure management. However, the optimal design of DMAs in a WDN is a challenging task. This paper proposes an approach for the optimal design of DMAs in the multiple-criteria decision analysis (MCDA) framework based on the outcome of a coupled model comprising a self-organizing map (SOM) and a community structure algorithm (CSA). First, the clustering principle of the SOM algorithm is applied to construct initial homologous clusters in terms of pressure and elevation. CSA is then coupled to refine the SOM-based initial clusters for the automated creation of multiscale and dynamic DMA layouts. Finally, the criteria for quantifying the performance of each DMA layout solution are assessed in the MCDA framework. Verifying the model on a hypothetical network and an actual WDN proved that it could efficiently create homologous and dynamic DMA layouts capable of adapting to water demand variability. [ABSTRACT FROM AUTHOR]

Water distribution networks (WDNs) comprise a complex network of pipes and are crucial for providing potable water to urban communities. Therefore, WDNs must be carefully managed to avoid problems such as water contamination and service failures; however, this requires a large budget. Because WDN components have different statuses depending on their installation year, location, transmission pressure, and flow rate, it is difficult to plan the rehabilitation schedule within budgetary constraints. This study, therefore, proposes a new pipe replacement scheduling approach to smooth the investment time series based on a life cycle cost (LCC) assessment for a large-scale WDN. The proposed scheduling plan simultaneously considers both the annual budget limitation and the optimum expenditure on the useful life of pipes. A multi-objective optimization problem consisting of three decision-making objectives—minimum imposed LCC on the network, minimum standard deviation of annual investment, and minimum average age of the network—is thus solved using a nondominated sorting genetic algorithm to obtain an optimal plan. Three scenarios with different pipe replacement time spans and different annual budget constraints are considered accordingly. The results indicate that the proposed scheduling framework provides an efficient water pipe replacement scheduling plan with a smooth management budget. [ABSTRACT FROM AUTHOR]

Contamination events in water distribution networks (WDNs) could have severe health and economic consequences. Contaminants can be deliberately or accidentally introduced into the WDN. Quick identification of the injection location and time is important in devising a mitigation plan to prevent further spread of the contaminant in the network. A method of identifying the possible intrusion point in a given network and reporting data is to use an inverse calculation by backtracking the potential path of the contaminant in the network. However, there is an element of uncertainty in the data used for calculation, particularly in water flow and sensor report time. Given the uncertainties, a method was developed in this study for fast and accurate contaminant source identification. This paper proposes a comparison filter of results by first identifying potential contaminant locations through backtracking, followed by a forward calculation to determine the injection time range, thereby reducing the potential suspects and providing likeliness comparison among the suspects. The effectiveness of the proposed method was examined by applying it to a benchmark WDN. By simulating uncertainties and filtering through the results, several possible contaminant intrusion locations and times were identified. [ABSTRACT FROM AUTHOR]

With recent increases of heavy rainfall during the summer season, South Korea is hit by substantial flood damage every year. To reduce such flood damage and cope with flood disasters, it is necessary to reliably estimate design floods. Despite the ongoing efforts to develop practical design practice, it has been difficult to develop a standardized guideline due to the lack of hydrologic data, especially flood data. In fact, flood frequency analysis (FFA) is impractical for ungauged watersheds, and design rainfall–runoff analysis (DRRA) overestimates design floods. This study estimated the appropriate design floods at ungauged watersheds by combining the DRRA and watershed characteristics using machine learning methods, including decision tree, random forest, support vector machine, deep neural network, the Elman recurrent neural network, and the Jordan recurrent neural network. The proposed models were validated using K-fold cross-validation to reduce overfitting and were evaluated based on various error measures. Even though the DRRA overestimated the design floods by 160%, on average, for our study areas the proposed model using random forest reduced the errors and estimated design floods at 99% of the FFA, on average. [ABSTRACT FROM AUTHOR]

The performance of water distribution networks (WDNs) can be quantified by several types of hydraulic measure. In design and operation of a WDN, sufficient consideration should be given to system performance, and it would be inefficient to separately consider individual characteristics of hydraulic measures. Instead, various reliability indices have been developed and utilized to evaluate the performance of WDNs; however, deciding which index to use according to a particular WDN situation has not been investigated in sufficient depth. In this regard, this study analyzes the correlation between representative reliability indices and hydraulic measures to propose the most adequate reliability index according to the desired system performance in various situations. Specifically, six hydraulic measures representing system performance were selected from the viewpoint of redundancy, robustness, and serviceability. In addition, nine indices for estimating system reliability were classified based on theoretical backgrounds such as hydraulic, topological, entropic, and mixed approaches. The correlations between the nine indices and six measures were analyzed using 17 sample hypothetical networks with different layouts, under three water supply scenarios, and the overall evaluation results for each reliability index are presented through multi-criteria decision analysis. [ABSTRACT FROM AUTHOR]

To restore water pipes damaged by earthquakes, it is common to block the water flow by closing the associated shut-off valves. In this process, water supply suspension in the area connected to the isolated pipes is inevitable, which decreases the serviceability of the water distribution network (WDN). In this study, we identified the impact of valve layout (i.e., number and location) on system serviceability during a seismic damage restoration process. By conducting a pressure-driven-analysis (PDA) using EPANET 3.0, a more realistic hydraulic analysis could be carried out under the seismically damaged condition. Furthermore, by considering the valve-controlled segment in the hydraulic simulation, a more realistic water suspension area was determined, and efficient seismic damage restoration strategies were identified. The developed model was implemented on a WDN to demonstrate the effect of valve layout on the post-earthquake restoration process. Finally, effective restoration strategies were suggested for the application network. [ABSTRACT FROM AUTHOR]

The increasing frequency of extreme droughts and flash floods in recent years due to climate change has increased the interest in sustainable water use and efficient water resource management. Because the water resource sector is closely related to human activities and affected by interactions between the humanities and social sciences, there is a need for interdisciplinary research that can consider various elements, such as society and the economy. This study elucidates relationships within the social and hydrological systems and quantitatively analyzes the effects of a multi-purpose dam on the target society using a system dynamics model. A causal loop was used to identify causal relationships between the social and hydrological components of the target area, and a simulation model was constructed using the system dynamics technique. Additionally, climate change and socio-economic scenarios were applied to analyze the future effects of the multi-purpose dam on population change, the regional economy, water use, and flood damage prevention in the target area. The model proved reliable in predicting socio-economic changes in the target area and can be used to make decisions about efficient water resource management and water-resource-related facility planning. [ABSTRACT FROM AUTHOR]

A water distribution network (WDN) is an indispensable element of civil infrastructure that provides fresh water for domestic use, industrial development, and fire-fighting. However, in a large and complex network, operation and management (O&M) can be challenging. As a technical initiative to improve O&M efficiency, the paradigm of "divide and conquer" can divide an original WDN into multiple subnetworks. Each subnetwork is controlled by boundary pipes installed with gate valves or flow meters that control the water volume entering and leaving what are known as district metered areas (DMAs). Many approaches to creating DMAs are formulated as two-phase procedures, clustering and sectorizing, and are called water network partitioning (WNP) in general. To assess the benefits and drawbacks of DMAs in a WDN, we provide a comprehensive review of various state-of-the-art approaches, which can be broadly classified as: (1) Clustering algorithms, which focus on defining the optimal configuration of DMAs; and (2) sectorization procedures, which physically decompose the network by selecting pipes for installing flow meters or gate valves. We also provide an overview of emerging problems that need to be studied. [ABSTRACT FROM AUTHOR]

The water–energy–food nexus (WEF nexus) concept is a novel approach to manage limited resources. Since 2011, a number of studies were conducted to develop computer simulation models quantifying the interlinkage among water, energy, and food sectors. Advancing a nationwide WEF nexus simulation model (WEFSiM) previously developed by the authors, this study proposes an optimization module (WEFSiM-opt) to assist stakeholders in making informed decisions concerning sustainable resource management. Both single- and multi-objective optimization modules were developed to maximize the user reliability index (URI) for water, energy, and food sectors by optimizing the priority index and water allocation decisions. In this study, the developed models were implemented in Korea to determine optimal resource allocation and management decisions under a plausible drought scenario. This study suggests that the optimization approach can advance WEF nexus simulation and provide better solutions for managing limited resources. It is anticipated that the proposed WEFSiM-opt can be utilized as a decision support tool for designing resource management plans. [ABSTRACT FROM AUTHOR]

In Table 1, the diameters of 15 pipes were printed incorrectly (P1, P7, P8, P9, P10, P11, P12, P14, P15, P18, P26, P34, P37, P38, and P41). Pipe information of the application networks. [Extracted from the article]