District Metered Areas Research Articles

Towards more realistic stochastic modelling of leakage in water distribution systems

ABSTRACT A new stochastic model is developed to predict leak types and sizes in water distribution systems. By linking records of actual failures in water networks with new tools for stochastic modelling, the study provides more accurate estimates of network leakage behaviour. District metered areas (DMAs) have been adopted internationally as best practice for monitoring and controlling leakage, with the infrastructure leakage index (ILI) as the main performance indicator. The leakage exponent (N1), which quantifies the relationship between leakage rate and pressure, is a key parameter in describing DMA leakage behaviour. After analysing thousands of stochastic networks for different combinations of pipe material and ILI, the study found that different pipe materials have distinct leakage exponent distributions. Iron and asbestos cement (AC) pipes display N1 values close to 0.5, polyvinyl chloride (PVC) pipes have the highest N1 values, and polyethylene (PE) lies between PVC and iron/AC. The study further showed that current guidelines linking ILI to N1 for different pipe materials depart substantially from the observed results, indicating that these guidelines may need to be updated. The study provides a tool for more realistic modelling of leakage that offers valuable insights into the behaviour of leakage in different pipe materials.

Pipeline Leak Identification and Prediction of Urban Water Supply Network System with Deep Learning Artificial Neural Network

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks.

Graph convolutional network for water network partitioning

The partitioning of a water distribution network into several district-metered areas (DMAs) yields is of great benefits for the management of water distribution systems, including enhanced leak control, pollution monitoring, and pressure optimization. However, achieving an effective partitioning that simultaneously incorporates node characteristics and network connections is challenging. Previous studies have tackled water network partitioning by focusing on either node features or pipe connections individually. In response to the limitations of current approaches for water distribution network partitioning, this study introduces an innovative unsupervised clustering approach for node partitioning based on graph convolutional network (GCN) techniques. Initially, the network nodes are clustered using the Cross-Attention Fusion Based Enhanced Graph Convolutional Network (CaEGCN) deep clustering algorithm, considering both the network’s topological layout and hydraulic properties. The resulting clusters categorize the network nodes into distinct regions. We evaluate our approach and other methods through a real world water distribution network using five different metrics commonly used to evaluate applied in assessing water supply network zoning issues. By comparing and analyzing the performance of experiments with other schemes, the experimental results demonstrate that the CaEGCN deep clustering method featured a remarkable performance. The proposed method showcases its potential for practical application by offering a dependable and easily interpretable water network partitioning solution.

Water Demand Forecasting in Multiple District Metered Areas Based on a Multi-Scale Correction Module Neural Network Architecture

Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand compared to overall water usage, accurately predicting STWDF poses significant challenges. This study introduces an innovative network architecture—the multi-scale correction module neural network, built upon Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) enhanced with Attention mechanisms—for simultaneous STWDF with a temporal resolution of one hour over a week for 10 DMAs located in a single city in northern Italy. This framework utilizes multivariate corrections to refine and enhance the output accuracy. The results reveal that, in comparison to traditional Gated Recurrent Unit or LSTM models, the proposed model with integrated correction modules, particularly those that leverage inter-DMA correlations, improves performance across all evaluation metrics by an average of 5%-20% per DMA. Additionally, it consistently delivers superior accuracy across three scenarios: single DMA forecasting, total water demand, and extreme conditions, while maintaining stable performance throughout. Furthermore, the interpretability analysis underscores the feasibility of this innovative structure and highlights the contribution of meteorological features to the predictive model in some DMA-level STWDF. The unified input-output framework elegantly simplifies the STWDF process across multiple DMAs, providing new insights and methodologies for future research in this domain.

Multi-level dynamic zoning design to improve the restorative capability of resilience: An emergence response in water contamination flushing

ABSTRACT The conventional district metered areas (DMAs) bounded by closed valves reduce space of emergency response actions to cope with system failure or emergency events for resilience improvement. In order to improve the restorative capability of a zoning water distribution system (WDS), a novel dynamic DMA optimization design method and a coupled emergency response strategy are proposed. A multi-level zoning method is applied to determine optimization of water source areas (WSAs), pressure management areas (PMAs), and dynamic DMAs. A coupled emergency response strategy of WDS zoning, valve closure, hydrant flushing, and dynamic DMA adjustment is proposed and validated in a real large-scale water distribution network. The results show that the coupled emergency response strategy based on dynamic DMA can enhance capacity for dynamic emergency response and improve resilience during contamination flushing.

Generating zonal hourly water consumption patterns in water distribution networks using end-use data

ABSTRACT patterns is a requisite for the design and operation of water distribution networks. This study uses end-use water consumption data to create zonal water consumption patterns. The process involves extrapolating end-use data to create flow variation in the water distribution network. In this study, the water consumption patterns of 30 household consumers as the end-use data were monitored for a period of one year. To validate the results of the presented methodology, zonal water consumption patterns of nine district-metered areas were also measured. The Normal and the Gumbel probability distributions were applied to individual water consumption patterns to generate comprehensive zonal water consumption patterns for different population sizes. The results indicate that the maximum Gumbel’s probabilistic distribution function with considering the leakage effect created better results. This innovative approach holds the potential to create comprehensive zonal water consumption patterns tailor-made for different population sizes.

Hydraulic Connectiveness Metric for the Analysis of Criticality in Water Distribution Networks

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.

An Improved DeNSE Methodology for Optimal Sectorization of Water Distribution Networks

Sectorization of a water distribution network (WDN) into district meter areas (DMAs) is considered a key strategy for an efficient management of WDNs. Typically, it involves a two-stage procedure: a clustering stage, in which the division of the WDN into clusters is performed, and a dividing stage, which involves the placement of valves and flow meters on the cluster’s boundary pipes to define the DMAs. While recently published methods attempt to enhance both the clustering and dividing stages, they fail to provide decision-making flexibility. They also neglect to consider the presence of existing valves in the WDN, which can significantly affect the evaluated implementation cost, often considered the primary decision-driving factor. This paper presents improvements to the previously introduced DeNSE method for sectorization of WDNs, aiming to address these deficiencies. The methodology consists of a clustering stage, based on the network uniformity index, and a dividing stage, in which the originally used heuristic procedure is replaced with Genetic Algorithm (GA) optimization, minimizing implementation cost. Consideration of existing valves in WDN and criteria for water supply security are also included in the dividing stage to offer a better estimate of implementation costs and post-sectorized operational efficiency of the WDN. Finally, GIS visualization is implemented, and a hydraulic model of the sectorized WDN (EPANET file) is generated, providing practitioners with valuable insights and decision-making flexibility. The methodology is tested on a part of the Amsterdam WDN in the Netherlands, serving as a pilot for methodology evaluation. A range of feasible sectorization solutions is generated and compared based on implementation cost and three performance indicators (PIs). The paper provides an in-depth discussion on the selection of preferable sectorization solution. The reported results demonstrate the method’s efficiency in optimizing sectorization solutions with minimum implementation cost whilst preserving the WDN operational efficiency and meeting the local design criteria.

Coupled multi-objective optimization of water distribution network design and partitioning: a spectral graph-theory approach

ABSTRACT This paper proposes a novel method that integrates a tailored spectral graph metric and economic aspects within a coupled bi-objective optimization framework for the simultaneous pipe sizing and partitioning of water distribution networks (WDNs). Besides minimum pressure head and topological WDN connectivity, the telescopic diameter distribution criterion is applied through a new algorithm in the sizing phase to help the optimization converge towards sound engineering solutions. The application of the proposed method to a synthetic layout and a real Italian case study proves the superiority of the solutions obtained with the novel method from hydraulic and managerial/operational viewpoints in comparison with those obtained with a traditional decoupled method (separate pipe sizing and partitioning) in terms of i) compliance with service pressure constraints and ii) ease of management following WDN partitioning into district metered areas (DMAs).

Burst detection in district metering areas using flow subsequences clustering–reconstruction analysis

ABSTRACT Frequent burst events in water distribution systems cause severe water loss and other environmental issues such as contamination and carbon emissions. The availability of massive monitored data has facilitated the development of data-driven burst detection methods. This paper proposes the flow subsequences clustering–reconstruction analysis method for burst detection in district metering areas (DMAs). The sliding window is used to create flow subsequence libraries for all time points of a day using a historical data set and thereafter the clustering–reconstruction analysis is conducted to obtain flow pattern libraries and reconstruction error subsequences. The threshold vector is determined by the detection matrix extracted from the reconstruction error subsequences at each time point. At the detection stage, the new flow subsequence is created and its reconstruction version is obtained based on the flow pattern library at the same time point. The new detection vector is extracted and compared with the threshold vector to identify bursts. The proposed method is applied to two real-world DMAs and its detection performance is demonstrated and compared with two previous methods. The proposed method is proven to be effective in detecting burst events with fewer false alarms.

Online burst detection in water distribution networks based on dynamic shape similarity measure

Monitoring water demand is extremely helpful in the early detection of issues and malfunctions in water distribution networks. Therefore, distinguishing abnormal water meter readings is an important problem in the management process. Although much focus has been given to this problem in the last few years, many of the approaches still lack efficiency and scalability when adapted to (near) real-time settings. To tackle these issues, we propose the Online Curvature Matcher, a real-time algorithm for the detection of pipe burst events in flow readings of District Metering Areas (DMA), which requires only a few weeks of data clean of burst events. In the proposed methodology, libraries of segments of the time series are constructed and then used to find abnormal patterns in consumption. We also propose the incorporation of additional constraints to address the complexities of real-world data (such as the existence of irrigation events, seasonal changes, and discrepancies in behavior due to weekday). We perform widespread testing on data from 32 DMA. The results indicate an increase in detected bursts compared to the state-of-the-art, while a decrease in the number of generated false positive events was achieved. Additionally, it was found that six weeks of history is the minimum amount of data necessary to perform online burst detection, while Dynamic Time Warping (DTW) is a suitable distance metric to measure the similarity between different flow reading segments.

DMA Characteristic Identification for Efficient Water Loss Management: Case Study of MWA Pipe Network, Thailand

This study is designed to categorize essential characteristics of District Metered Area (DMA), focusing on aspects related to pipe management such as real water loss, pipe density, and customer density. Our objective is to delineate the influence of each characteristic, with the ultimate goal of developing a practical index that aids planners in making informed decisions about water loss management. To achieve this, we propose the Area Characteristic Index (ACI), calculated using a fuzzy logic system, which amalgamates these characteristics to provide a comprehensive indicator of DMA performance. The results of this study elucidate the distinctive advantages of considering various characteristics when evaluating DMAs with diverse features. Specifically, real losses- a key element within the water balance framework-exert a more substantial influence on the ACI compared to the other characteristics assessed. Furthermore, to determine the number of deteriorated pipes in each DMA, we computed a Risk Index (RI) and an Infrastructure Value Index (IVI). The findings suggest that the extended length of deteriorated pipes, attributed to RI pipes, influences the ACI value and real losses significantly. Concluding, this study introduces a prospective matrix for pipe network management, serving as a guideline to enhance the efficiency of water loss management practices.

District metered areas determination for water distribution networks using improved Girvan-Newman algorithm

In general, partitioning networks into district metered areas (DMA) is known as a practical method to manage the pressure and reduce water leakage in Water Distribution Networks (WDNs). Different methods such as engineering judgment, optimization techniques, and graph theory are proposed to determine the DMA. The graph theory is a traditional method for partitioning networks including several algorithms. One of these algorithms is the Girvan-Newman (GN) algorithm which is based on the mathematical parameters without considering the characteristics of networks. In other words, the hydraulic quality and quantity condition of WDNs are not used for DMA determination. Therefore, in this research, a new method is proposed to improve the performance of the GN algorithm for DMA determination considering the quantities of water networks. For this purpose, the average values of nodal pressure and residual chlorine concentration are calculated and used simultaneously for the junction’s weight determination. Then, the edge scores are calculated based on the junction’s weights, and the edges with the maximum scores are removed until to reach the desired number of DMAs. For comparison purposes, here, the Demand-Driven Simulation Method (DDSM) and Head-Driven Simulation Method (HDSM) analyses are used for the analysis of the Poulakis WDN, selected as a case study. A comparison of the results shows that by using the proposed method, the average pressure values are decreased and the average residual chlorine concentration values are increased. In other words, the pressure values are decreased from 16.32% to 26.23% and the average residual chlorine concentration values are increased from 2.5% to 18.37% in comparison with the results of the standard form of the GN algorithm using both DDSM and HDSM analyses. Furthermore, by using the proposed method, in all scenarios, the hybrid reliability values increase in comparison with the GN algorithm. The results indicate the unique performance of the proposed method in comparison with the standard form of the GN algorithm for DMA determination of WDNs.

Hydraulic Modelling for Leakage Reduction in Water Distribution Systems Through Pressure Control

Background Leakage in a water distribution network makes up a significant amount of loss. In certain countries, this loss represents 40% to 50% of the supplied water, whereas the global average for most systems is estimated at around 30%. Furthermore, water demand is increasing as a result of population growth, while resources are dwindling. Methodology This study used hydraulic modelling for leakage reduction through pressure control. For this purpose, a hydraulic model was created using the software EPANET for a selected water distribution network in a district-metered zone in Jeddah. The model was calibrated and validated for the district-metered zone using data obtained by SCADA. Pressure management using a pressure-reduction valve was then implemented to control the amount of leakage in water distribution systems. Results The preliminary findings show that pressure optimization was required as there were nodes within the network that had excessive pressure. Application of pressure management to the district metered zone showed that the reduction in water pressure from 5 bar to 2 bar resulted in an immediate reduction in water losses. The leakage volume through the district-metered area at the time of maximum pressure dropped by 10% after pressure reduction. Simultaneously, the minimum required pressure was upheld at each demand node, preventing any lack of pressure in the water distribution system. Conclusion The results indicated that pressure control should be integrated with hydraulic modelling for effective leakage reduction. This research could help water-supply companies as a support tool for planning and managing leakage in water distribution systems.

Determination of the most economical leakage level in the district-metered area with the optimization algorithm

Determination of the most economical leakage level in the district-metered area with the optimization algorithm

Cluster Analysis to Identify Priority Areas for Water Quality Management in Water Supply Systems

Zoning methodologies, such as district metered areas (DMA), are commonly employed to robustly maintain water pipe network systems in both normal and abnormal situations. However, the management and evaluation typically associated with existing DMAs primarily focus on ensuring stable water volume and pressure. Consequently, these methods do not adequately address the maintenance of water quality elements within the water supply system, such as adequately managing residual chlorine and reducing water quality complaints. This study introduced a zoning tailored explicitly for managing water quality-oriented elements, facilitating stable water quality management, and enhancing responses to water quality incidents in large-scale domestic water supply networks. A method was proposed to establish priorities for each zone, using various geographic information system (GIS)-based water quality-related structured data (such as water quality measurement data and pipe data) and unstructured data (such as water quality complaints). Comprehensive water quality management was achieved by applying machine learning techniques based on clustering analysis to derive evaluation factors. The proposed methodology was implemented in Metropolitan City A in Korea, leading to the derivation and analysis of evaluation results. This data-centric water supply network priority management area designation methodology, as presented in this study, is anticipated to serve as a valuable decision-making tool for enhancing the accuracy and reliability of water supply network operation and the overall management of water supply operators.

Evaluation of the Primary Pipe Network at Banua Anyar Booster Pump as an Effort to Reduce Water Loss in the Service area of Northern Banjarmasin, South Kalimantan

Recorded as the best PDAM in South Kalimantan in Banjarmasin City. PT. Bandarmasih Perseroda Drinking Water (PAM Bandarmasih) is still facing the problem of high water loss rates of 29.07% in 2022, which is still far above the National Medium Term Development Plan (RPJMN) target with a minimum water loss value of 20%. One of the ways PDAM can reduce water loss is by evaluating the pipe network in a District Meter Area (DMA). This research focuses on evaluating the primary pipe network in the area served by the Banua Anyar booster which serves 18 DMAs. Network evaluation using EPANET 2.0 software. The evaluation results show that the diameter of the primary pipe from the booster pump to the Banua Anyar pipe bridge has quite high unit-head loss and flow velocity values due to flow friction with the pipe surface. It is necessary to change the diameter of the primary pipe used in the Banua Anyar booster network from a diameter of 400 mm to 600 mm which can reduce the rate of water loss by 6.3%. In the future, it is hoped that the value of water loss at PDAM Banjarmasin City can be reduced and the affordability of drinking water services for the people of Banjarmasin City can increase.

Dynamic analysis of non-revenue water in district metered areas under varying water consumption conditions owing to COVID-19

Increasing water demands and high water losses have rendered securing safe water challenging in the 21st century. Although non-revenue water (NRW), as a percentage of system input, has been commonly used by water utilities worldwide, in-depth analyses on the influence of water consumption fluctuation on NRW has never been conducted; instead, taking one-year average NRW volume has been recommended. Thus, this study analyzed the influence of water consumption fluctuation on NRW using the data of five district metered areas (DMAs) in Colombo City, Sri Lanka, and also by the network simulation analysis. The results showed that percentage and volumetric NRWs are strongly correlated with water consumption (r = 0.9373 and 0.9121, respectively) and with each other (r = 0.9977) due to pressure changes in water supply networks caused by water consumption fluctuation. Therefore, dynamic analysis of NRW by plotting DMA inflow and NRW against water consumption was conducted using the aforementioned DMA data and long-term (1956–2021) water consumption and NRW data in Tokyo. This method identified two factors influencing NRW: water consumption fluctuation and network leakage changes, and the results were verified; thus, it can be applied to NRW analysis even under the influence of high water consumption fluctuations.

Domain-informed variational neural networks and support vector machines based leakage detection framework to augment self-healing in water distribution networks

The reduction of water leakage is essential for ensuring sustainable and resilient water supply systems. Despite recent investments in sensing technologies, pipe leakage remains a significant challenge for the water sector, particularly in developed nations like the UK, which suffer from aging water infrastructure. Conventional models and analytical methods for detecting pipe leakage often face reliability issues and are generally limited to detecting leaks during nighttime hours. Moreover, leakages are frequently detected by the customers rather than the water companies. To achieve substantial reductions in leakage and enhance public confidence in water supply and management, adopting an intelligent detection method is crucial. Such a method should effectively leverage existing sensor data for reliable leakage identification across the network. This not only helps in minimizing water loss and the associated energy costs of water treatment but also aids in steering the water sector towards a more sustainable and resilient future. As a step towards ‘self-healing’ water infrastructure systems, this study presents a novel framework for rapidly identifying potential leakages at the district meter area (DMA) level. The framework involves training a domain-informed variational autoencoder (VAE) for real-time dimensionality reduction of water flow time series data and developing a two-dimensional surrogate latent variable (LV) mapping which sufficiently and efficiently captures the distinct characteristics of leakage and regular (non-leakage) flow. The domain-informed training employs a novel loss function that ensures a distinct but regulated LV space for the two classes of flow groupings (i.e., leakage and non-leakage). Subsquently, a binary SVM classifier is used to provide a hyperplane for separating the two classes of LVs corresponding to the flow groupings. Hence, the proposed framework can be efficiently utilised to classify the incoming flow as leakage or non-leakage based on the encoded surrogates LVs of the flow time series using the trained VAE encoder. The framework is trained and tested on a dataset of over 2000 DMAs in North Yorkshire, UK, containing water flow time series recorded at 15-minute intervals over one year. The framework performs exceptionally well for both regular and leakage water flow groupings with a classification accuracy of over 98 % on the unobserved test dataset

Water Distribution System Management in District Metered Area (DMA) Kalipuro 5

Water supply system is an important infrastructure that needs improvement in water distribution networks efficiency, along with energy consumption and water losses. DMA Kalipuro 5 is formed to improve the management of water distribution system by monitoring the water flows into the district. This program is supported by pressure management, repairs, active leakage control, and asset management. Water balance is the first step to reduce the water losses. From the water balance of DMA Kalipuro 5 calculated in January and September 2022, the percentage of water losses decreases 7,02%, from 62,55% to 55,53%. The percentage of physical water losses also decreases 7,06%, from 62,32% to 55,26%. Pressure management is conducted in order to reduce the physical water losses by installing pressure reducing valve (PRV). From the simulation of existing water distribution network, installing PRV can reduce the highest pressure from 108,82 meters to 58,42 meters.