Showing papers in "Journal of Water Resources Planning and Management in 2022"
TL;DR: In this paper , the authors proposed a hierarchical approach to detect multiple leaks simultaneously in a water distribution network for the Battle of the Leak Detection and Isolation Methods, where a combination of time series and cluster analysis is used to build patterns for demand models.
Abstract: Water utilities are challenged to reduce their water losses through detecting, localizing, and repairing leaks as quickly as possible in their aging distribution systems. In this work, we solve this challenging problem by detecting multiple leaks simultaneously in a water distribution network for the Battle of the Leak Detection and Isolation Methods. The performance of leak detection and localization depends on how well the system roughness and demand are calibrated. In addition, existing leaks affect the diagnosis performance unless they are identified and explicitly represented in the model. To circumvent this chicken-and-egg dilemma, we decompose the problem into multiple levels of decision-making (a hierarchical approach) where we iteratively improve the water distribution network model and so are able to solve the multileak diagnosis problem. First, a combination of time series and cluster analysis is used on smart meter data to build patterns for demand models. Second, point and interval estimates of pipe roughnesses are retrieved using least squares to calibrate the hydraulic model, utilizing the demand models from the first step. Finally, the calibrated primal model is transformed into a dual model that intrinsically combines sensor data and network hydraulics. This dual model automatically converts small pressure deviations caused by leaks into sharp and localized signals in the form of virtual leak flows. Analytical derivations of sensitivities with respect to these virtual leak flows are calculated and used to estimate the leakage impulse responses at candidate nodes. Subsequently, we use the dual network to (1) detect the start time of the leaks, and (2) compute the Pearson correlation of pressure residuals, which allows further localization of leaks. This novel dual modeling approach resulted in the highest true-positive rates for leak isolation among all participating teams in the competition.
19 citations
TL;DR: In this paper , a review of existing basin-level models and decision support systems (DSSs) for public water allocation, provides application examples, and systematically reviews the literature on concepts arising from the definition of integrated water resources management (IWRM).
Abstract: Integrated Water Resources Management (IWRM) is increasingly important due to water scarcity, population growth, climate change, and deterioration of resource quality. IWRM requires analytical tools such as automated models and interactive systems, which may be difficult to implement, particularly in developing countries. This paper reviews existing basin-level models and decision support systems (DSSs) for public water allocation, provides application examples, and systematically reviews the literature on concepts arising from the definition of IWRM. Two environmental concepts considered in this review, water quantity-quality management and sustainability, were the most frequently used in revised allocation models, most of these in spatial decision support systems (SDSS). These automated systems presented, in most cases, three advantages: a well-developed friendly interface enabling application of more than one model, allowance for different decision criteria and restrictions together with the analysis of scenarios/sensitivity, and tight coupling and connection to spatial databases. This greatly facilitates and enhances their use by decision makers and stakeholders, favoring an environmentally sustainable management of water resources. Findings showed few models that combined the requirement of sustainable management maximizing economic well-being together with equality. Overall, most of the tools developed are prescriptive (optimization), nonlinear and deterministic models, which were not available through any DSS. In addition, allocation modeling that considers aspects of quantity-quality combined with economic optimization was almost entirely developed in the last decade of the analyzed period. Implementation of IWRM needs a process with participation of all the stakeholders involved, supported by hydrological and economic models integrated and available through DSS.
15 citations
TL;DR: Integrated Water Resources Management (IWRM) is increasingly important due to water scarcity, population growth, climate change, and deterioration of resource quality as discussed by the authors, and IWRM requires analyti...
Abstract: Integrated Water Resources Management (IWRM) is increasingly important due to water scarcity, population growth, climate change, and deterioration of resource quality. IWRM requires analyti...
15 citations
TL;DR: In this paper , a literature review is presented to develop a step-by-step analytic framework for the leakage detection process based on flow and pressure data collected from water distribution networks and the main steps of the data analytic for leakage detection are: setting up the goals, data collection, preparing the gathered data, analyzing the prepared data, and method evaluation.
Abstract: Leakage detection is one of the important aspects of water distribution management. Water companies are exploring alternative approaches to detect leaks in a timely manner with high accuracy to reduce water losses and minimize environmental and economic consequences. In this article, a literature review is presented to develop a step-by-step analytic framework for the leakage detection process based on flow and pressure data collected from water distribution networks. The main steps of the data analytic for leakage detection are: setting up the goals, data collection, preparing the gathered data, analyzing the prepared data, and method evaluation. The issues of concern for each step of the proposed leakage detection framework are analyzed and discussed. The smart sensor-based leakage detection methods can be categorized as data-driven methods and model-based methods. Data-driven methods can be further categorized as statistical process control-based methods, prediction-classification methods, and clustering methods. Hydraulic model-based methods can be further categorized as calibration-based methods, sensitivity analysis, and classifier-based methods. The advantages and disadvantages of each method are discussed, and suggestions for future research are provided. This review represents a new perspective on the subject from five aspects: (1) most of the leakage detection methods are focused on burst detection, and different types of leakage should be considered in future research, (2) it is important to consider data uncertainties, and more robust real-time leakage detection methods should be developed, (3) it is important to consider hydraulic model uncertainties, (4) unrealistic assumptions should be addressed in future research, and (5) spatial relations between sensors could provide more information and should be considered.
14 citations
TL;DR: The leakage identification and localization algorithm (LILA) as discussed by the authors identifies potential leakages via semisupervised linear regression of pairwise sensor pressure data and provides the location of their nearest sensors.
Abstract: Leakages in water distribution networks (WDNs) are estimated to globally cost 39 billion USD/year and cause water and revenue losses, infrastructure degradation, and other cascading effects. Their impacts can be prevented and mitigated with prompt identification and accurate leak localization. In this work, we propose the leakage identification and localization algorithm (LILA), a pressure-based algorithm for data-driven leakage identification and model-based localization in WDNs. First, LILA identifies potential leakages via semisupervised linear regression of pairwise sensor pressure data and provides the location of their nearest sensors. Second, LILA locates leaky pipes relying on an initial set of candidate pipes and a simulation-based optimization framework with iterative linear and mixed-integer linear programming. LILA is tested on data from the L-Town network devised for the Battle of Leakage Detection and Isolation Methods. Results show that LILA can identify all leakages included in the data set and locate them within a maximum distance of 374 m from their real location. Abrupt leakages are identified immediately or within 2 h, while more time is required to raise alarms on incipient leakages.
14 citations
TL;DR: In this article, a pragmatic approach for leakage detection and localization is presented to solve the problem introduced within the framework of the Battle of the Leakage Detection and Isolaization.
Abstract: In this paper, a pragmatic approach for leakage detection and localization is presented to solve the problem introduced within the framework of the Battle of the Leakage Detection and Isola...
13 citations
13 citations
TL;DR: In this paper , a pragmatic approach for leakage detection and localization is presented to solve the problem introduced within the framework of the Battle of the Leakage Detection and Isolation Methods (BattLeDIM), based on the application of the hydraulic model of the water distribution network and the comparison of simulated pressures against the corresponding values observed in field.
Abstract: In this paper, a pragmatic approach for leakage detection and localization is presented to solve the problem introduced within the framework of the Battle of the Leakage Detection and Isolation Methods (BattLeDIM). The method is based on the application of the hydraulic model of the water distribution network and the comparison of simulated pressures against the corresponding values observed in field. In particular, it consists of two phases: (1) calibration of the hydraulic model of the network; and (2) detection and localization of the leakages affecting the water distribution network through the application of engineering judgment and the adoption of an enumerative procedure. The method was applied to the case-study network of L-Town, enabling 16 of 23 leakages to be efficiently detected and 11 of these also accurately localized. The proposed method is simple and transparent and can aid water utilities in water leakage management.
13 citations
TL;DR: In this article , a graph neural network (GNN) model is proposed for state estimation in water distribution systems (WDSs), which can learn from graph structure with a limited amount of information while exhibiting robustness to noise.
Abstract: Emerging trends of resilient and reliable water infrastructure advocate for the development of efficient state estimation (SE) techniques in water distribution systems (WDSs). SE refers to estimating the flows and heads in the WDS at unmonitored locations based on measurements collected from limited monitoring locations. Current physics-based SE methods typically require more exhaustive than readily available information about the WDS and are computationally demanding to attain real-time SE fully. Using neural networks for SE is a promising avenue because neural networks are more adaptable to the availability of sensory data and can shift most of the computation efforts to the offline training phase. Once trained, the inference is more computationally efficient compared to the physics-based SE methods. This work proposes a graph neural network (GNN) model for SE in WDSs. Unlike traditional neural networks, GNNs are more suitable for the SE problem for two main reasons: (1) given a limited number of monitoring locations, the SE problem inherently requires a semisupervised learning method, and (2) GNNs enable learning from the graph structure of a WDS, thus providing a mechanism to incorporate the functional relationships between the monitored and unmonitored locations and incorporate the physical laws during the training process. To evaluate the performance of GNNs for SE, we tested supervised and semisupervised approaches, investigated the impact of GNN architecture choices on its performance, and examined model performance under different levels of noise in the training data. The results demonstrate that GNNs are promising for SE for their ability to learn from graph structure with a limited amount of information while exhibiting robustness to noise. This study contributes toward advancing real-time GNN-based SE in WDSs. Future research is needed to incorporate various hydraulic devices and investigate the scalability of GNNs to large-scale WDSs.
12 citations
TL;DR: In this article , a third-order algorithm for WDNs is proposed, which is based on the direct pressure-driven formulation expressing outflows as a function of service pressure and is equipped with dampening of the Newton Raphson step.
Abstract: This paper presents a novel algorithm with improved convergence and robustness for the pressure-driven modeling of water distribution networks (WDNs), to be implemented as hydraulic engine in the fourth release of the SWANP version 4.0 software. The innovative approach is based on increasing the order of convergence, which is quadratic for algorithms obtained from the Newton Raphson linearization of the equations for WDN resolution. As an example, the cubic order of convergence is obtained by evaluating system matrices at the generic iteration in a more refined way to account for the curvature of the hyperplane associated with the system in the direction of the Newton Raphson step. To show the benefits of the methodology, a third-order algorithm is constructed and compared with a traditional second-order. Both algorithms are based on the direct pressure-driven formulation expressing outflows as a function of service pressure and are equipped with the dampening of the Newton Raphson step. Applications on two case studies of different size, in which challenging pressure-driven conditions are created through demand amplification and segment isolation scenarios, prove that the methodology always reduces the total number of iterations required for convergence and the application of the step dampening. Overall, the results also show that the more stable convergence behavior is accompanied by an appreciable reduction in computation times. Further analyses proved that the third-order algorithm has similar convergence properties to algorithms based on the inverse pressure-driven formulation recently proposed in the scientific literature and can therefore be considered as a valid alternative to these algorithms.
11 citations
TL;DR: A leak localization method that requires hydraulic measure- 18 ments and structural information of the network and a recursive clustering/learning approach is proposed, comparing its performance with another state-of-the-art technique and demonstrating the capability of the method to regulate the area of localization depending on the depth of the route through the tree.
Abstract: 16 Leak detection and localization in water distribution networks (WDNs) is of great significance 17 for water utilities. This paper proposes a leak localization method that requires hydraulic measure- 18 ments and structural information of the network. It is composed by an image encoding procedure 19 and a recursive clustering/learning approach. Image encoding is carried out using Gramian Angular 20 Field (GAF) on pressure measurements to obtain images for the learning phase (for neural network (DNN) to discern the location of each leak between the two possible clusters, 24 using each one of them as inputs to future iterations of the process. The achieved set of DNNs is 25 hierarchically organized to generate a classification tree. Actual measurements from a leak event 26 occurred in a real network are used to assess the approach, comparing its performance with another 27 state-of-the-art technique, and demonstrating the capability of the method to regulate the area of 28 localization depending on the depth of the route through the tree. 29
TL;DR: 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 as discussed by the authors .
Abstract: 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.
TL;DR: In this article, a stochastic approach for modeling and analysing the transients due to the users' water consumptions in a real water distribution system is presented, based on field measure.
Abstract: A stochastic approach for modeling and analysing the transients due to the users’ water consumptions in a real water distribution system is presented. The analysis is based on field measure...
TL;DR: In this paper , a stochastic approach for modeling and analysing the transients due to the users' water consumptions in a real water distribution system is presented, based on field measurements of water consumption at each user and pressure at three nodes, acquired at 1 min and 0.01 s time step, respectively.
Abstract: A stochastic approach for modeling and analysing the transients due to the users’ water consumptions in a real water distribution system is presented. The analysis is based on field measurements of water consumption at each user and pressure at three nodes, acquired at 1 min and 0.01 s time step, respectively. The hydraulic numerical model used is based on the method of characteristics and includes the unsteady friction. Several scenarios of water consumptions at 1-s time step are generated starting from those observed. The corresponding pressure variation scenarios are given by the numerical model and stochastically compared with the measured values. The analysis of the results shows that the approach is capable of stochastically reproducing the dynamic behavior of the system. Specifically, the generated water consumption scenarios with random maneuvering times allow properly reproducing the main statistics (mean, variance, and minimum and maximum values) of the observed pressures. Finally, the average cumulative distribution of the simulated pressure viably simulates the cumulative distribution of the observed ones from a stochastic point of view.
TL;DR: The 2019 Battle of the Leakage Detection and Isolation Methods (BattLeDIM) competition as mentioned in this paper was organized 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 virtual water distribution system.
Abstract: 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.
TL;DR: In this article , the authors presented a methodology for the active inspection of pipelines using convolutional neural networks (CNNs) to detect leaks and anomalies in water pipelines using stochastic resonance.
Abstract: Water losses through leakage represent a significant problem for asset management in water distribution systems. The interpretation of fluid transient pressure waves after the generation of a transient event has been previously used as a technique to locate and characterize leaks, but existing approaches are often both model-driven and limited to the existing knowledge of the system. The potential of using artificial neural networks (ANN) and fluid transient waves to detect, locate, and characterize anomalies in water pipelines has recently been proposed. However, its application in more realistic conditions (e.g., in the presence of background pressure fluctuations) has proven challenging. To address this, one alternative to enhance the response of any nonlinear system includes the introduction of artificial noise, a phenomenon known as stochastic resonance. In this paper, the enhanced detection of leaks in pressurized pipelines via the deployment of stochastic resonance is demonstrated. This paper harnesses this approach by presenting a methodology for the active inspection of pipelines using convolutional neural networks (CNNs). This methodology finds the optimal artificial noise intensity to be introduced into the training dataset for a set of CNNs. The methodology has been applied to a real pipeline in a laboratory at the University of Adelaide in which 14 transient experimental tests were conducted. The results indicated that the addition of noise to the transient pressure head training samples significantly enhances the CNN predictions for the leak location highlighting the existence of an optimum noise intensity to obtain both accurate and reliable results. When trained with the optimum noise intensity, the CNNs were able to locate leaks with an average error of 0.59% in terms of the actual location (in a 37.24-m long pipeline), demonstrating the promising potential of developing techniques based on CNNs to detect leaks and anomalies in water pipelines.
TL;DR: In this article , an iterative heuristic is proposed to improve the distribution of isolation valves within an existing water distribution network to decrease the magnitude of service interruptions while using the minimum number of valves required to isolate any individual segment within the system.
Abstract: An iterative heuristic is proposed to improve the distribution of isolation valves within an existing water distribution network to decrease the magnitude of service interruptions while using the minimum number of valves required to isolate any individual segment within the system. The method takes advantage of graph theory concepts to create a valve augmentation scheme that provides gradual upgrades using the minimum number of new isolation valves at each step. The developed algorithm seeks to provide a tradeoff between an increase in the number of isolation valves and a reduction in water shortages resulting from disconnected pipe segments. The approach is applied to an actual water distribution network with known existing valve locations. The results demonstrate the feasibility and utility of the procedure for multiple operational constraints (i.e., maximum number of valves per segment, or maximum allowable water shortage evaluated over all segments). By use of an incremental performance target set by the user, the algorithm can prove beneficial even for utilities with limited financial resources.
TL;DR: In this paper , the authors report on how the operations of 27 water utilities worldwide were affected by the COVID-19 pandemic, and 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.
Abstract: 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.
TL;DR: In this paper , an agent-based modeling framework is developed to explore social dynamics and reactions of water consumers and a utility manager to a contamination event, while considering regular and pandemic demand scenarios.
Abstract: Contamination events in water distribution systems (WDS) are emergencies that cause public health crises and require fast response by the responsible utility manager. Various models have been developed to explore the reactions of relevant stakeholders during a contamination event, including agent-based modeling. As the COVID-19 pandemic has changed the daily habits of communities around the globe, consumer water demands have changed dramatically. In this study, an agent-based modeling framework is developed to explore social dynamics and reactions of water consumers and a utility manager to a contamination event, while considering regular and pandemic demand scenarios. Utility manager agents use graph theory algorithms to place mobile sensor equipment and divide the network in sections that are endangered of being contaminated or cleared again for water consumption. The status of respective network nodes is communicated to consumer agents in real time, and consumer agents adjust their water demands accordingly. This sociotechnological framework is presented using the overview, design, and details protocol. The results comprise comparisons of reactions and demand adjustments of consumers to a water event during normal and pandemic times, while exploring new methods to predict the fate of a contaminant plume in the WDS.
TL;DR: In this article , a model-based approach that uses the hydraulic model of the network, as well as pressure and demand information; and a fully data-driven method based on graph interpolation and a new candidate selection criteria were proposed.
Abstract: The detection and localization of leaks in water distribution networks (WDNs) is one of the major concerns of water utilities, due to the necessity of an efficient operation that satisfies the worldwide growing demand for water. There exists a wide range of methods, from equipment-based techniques that rely only on hardware devices to software-based methods that exploit models and algorithms as well. Model-based approaches provide an effective performance but rely on the availability of an hydraulic model of the WDN, while data-driven techniques only require measurements from the network operation but may produce less accurate results. This paper proposes two methodologies: a model-based approach that uses the hydraulic model of the network, as well as pressure and demand information; and a fully data-driven method based on graph interpolation and a new candidate selection criteria. Their complementary application was successfully applied to the Battle of the Leakage Detection and Isolation Methods (BattLeDIM) 2020 challenge, and the achieved results are presented in this paper to demonstrate the suitability of the methods.
TL;DR: The authors analyzed the US municipal bond market and the bond offerings of 25 Pennsylvania water utilities over a 30-year period to understand how financial conditions and risk have evolved for local government water utilities.
Abstract: Local governments in the US routinely provide water services drinking water delivery and wastewater treatment. After the Great Depression, the federal government shared the financial responsibility for water services. However, over the last 3 decades, federal support has declined, exposing local governments to greater financial risk. We analyzed the US municipal bond market and the bond offerings of 25 Pennsylvania water utilities over a 30-year period to understand how financial conditions and risk have evolved for local government water utilities. The financial health of water utilities in the US is affected by monetary policy, federal tax policy, the broader financial (bond) market, regional corporate/industrial activity, and increased pressure to maintain credit quality. Utilities in our case study are persisting with a delicate balance of low interest rates for municipal bond issuances, moderate credit downgrades, and raising water rates to maintain credit quality. If one or more of these forces slips to a greater extent (e.g., higher interest rates, larger credit downgrades, or rising affordability crises), utilities could be left with few options to sustain the fiscal solvency needed to ensure safe and reliable water services. Water managers and engineers can use these findings to understand better how federal funding and policy, central bank decisions, and market sentiment affect municipal utilities and the population that they serve.
TL;DR: In this paper , a multiobjective optimization model for adding optimally located isolation valves to old WDNs, which considers the dual objectives of economy and reliability, was proposed, and the optimization model was applied to the valve layout modification of part of the WDN in Changshu, Jiangsu Province, China.
Abstract: There are often planned (for example, regular maintenance) and unplanned (for example, pipe bursts) interruptions in a water distribution network (WDN). Therefore, part of the isolation valves must be closed to isolate the part (segment) of the network that contains one or more pipes. Isolation of the target pipe segment with minimum possible disruption has been a problem to be solved. A large number of studies have been conducted to optimize the design of isolation valve placement in new WDNs, but less attention has been given to reducing the isolation zone and improving the reliability of old WDNs by adding optimally placed isolation valves. Therefore, this paper proposes a multiobjective optimization model for adding optimally located isolation valves to old WDNs, which considers the dual objectives of economy and reliability. The installation or removal of isolation valves can cause the original segments to split or merge, so this paper proposes the use of the segment-valve (SV) graph local update (SVLU) algorithm instead of the seed-filling algorithm to construct the SV graph. The optimization model was applied to the valve layout modification of part of the WDN in Changshu, Jiangsu Province, China, and the results showed that the model can solve the optimization solution quickly (15.358 s). Moreover, the use of the SVLU algorithm improved the efficiency of the solved model by 26.71%.
TL;DR: In this article , robust optimization and info-gap decision theory combined with a cuckoo search optimization algorithm were proposed to solve the problem of water quality uncertainty in the WDS design problem.
Abstract: 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.
TL;DR: In this article, the authors discuss the problem of detecting and quantifying apparent water losses, which is very difficult to detect and quantify, and is often associated with water meter anomalies.
Abstract: Apparent water losses can be problematic to water companies’ revenues. This type of loss is very difficult to detect and quantify and is often associated with water meter anomalies. This st...
TL;DR: In this article , a combination of statistical methods is used to detect decreasing water usage patterns, contributing to meter performance assessment, and a quantitative indicator of this change is proposed. But this type of loss is very difficult to detect and quantify and is often associated with water meter anomalies.
Abstract: Apparent water losses can be problematic to water companies’ revenues. This type of loss is very difficult to detect and quantify and is often associated with water meter anomalies. This study was motivated by a water company’s challenge that links a decrease in water consumption to water meters’ malfunction. The aim is to develop a strategy to detect decreasing water usage patterns, contributing to meter performance assessment. The basis of the approach is a combination of statistical methods. First, the time series of billed water consumption is decomposed using Seasonal-Trend decomposition based on Loess. Next, breakpoint analysis is performed on the seasonally adjusted time series. After that, the Mann–Kendall test and Sen’s slope estimator are used to analyze periods of progressive decrease changes in water consumption, defined by breakpoints. A quantitative indicator of this change is proposed. The strategy was successfully applied to eight-time series of water consumption from the Algarve, Portugal.
TL;DR: In this article , a new convex heuristic was developed to optimally place and operate valves and chlorine boosters in water networks, while estimating the optimality gaps for the computed solutions.
Abstract: 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.
TL;DR: Wang et al. as mentioned in this paper proposed a cascading framework to quantify the impacts of climate change on the operational performance and sustainability of a multipurpose reservoir, which serves as the water source for the middle route of the South-to-North Water Diversion Project in China.
Abstract: Climate change impacts on hydrological processes can affect reservoir operational performance. Hence, the reservoir operation model, based on historical climate conditions, may not guarantee sustainable water resources management in the future. To enable stakeholders to design reliable adaptation strategies, this study aims to propose a cascading framework to quantify the impacts of climate change on the operational performance and sustainability of a multipurpose reservoir. The Danjiangkou Reservoir (DJKR), which serves as the water source for the middle route of the South-to-North Water Diversion Project in China, was selected as a case study. To achieve the aforementioned aims, bias-corrected simulations from 13 global climate models (GCMs) were first input into five hydrological models [i.e., one data-driven [deep belief network (DBN)], three conceptual [SIMHYD, HBV, and Xin’anjiang (XAJ)], and one physically-based [variable infiltration capacity (VIC)]. The simulated reservoir inflows were then fed into a 10-day reservoir simulation model where DJKR operation followed the designed operating rules to evaluate reservoir operational performance. Finally, a data envelopment analysis (DEA) model was proposed to assess reservoir sustainability under both historical (1976–2005) and future (2021–2050) climate conditions. The results show that the combination of the GCM ensembles and the SIMHYD, HBV, XAJ, and VIC models exhibit similar growth patterns in the reservoir inflow and operational benefits for the future period. However, the DBN model produces consistent decreases in most cases, which may be attributed to its inability to generate accurate estimates of extreme events. The results indicate that hydrological models may be extensively utilized in decision making with greater confidence, and the data-driven model should be interpreted with caution when used in hydrological climate change impact studies. The efficiency metrics suggest that decision makers should focus more on increasing operational benefits, which can subsequently enhance reservoir sustainability. Overall, the framework proposed in this study provides a foundation for evaluating the reservoir sustainability and adaptability to climate change from water managers’ perspective.