Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines

Zhang, Qingzhou; Wu, Zheng Yi; Zhao, Ming; Qi, Jingyao; Huang, Yuan; Zhao, Honglin

doi:10.1061/(asce)wr.1943-5452.0000661

Cited by 118 publications

(79 citation statements)

References 31 publications

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“…We test the proposed methodology in a medium-size water distribution system located in southern China, H Town [39], which owns one fixed-head reservoir, 898 demand nodes, and 1012 pipes. Under the peak hour demand scenario, a minimum service pressure of 24 m is required-more details can be referred to Table 2.…”

Section: Resultsmentioning

confidence: 99%

An Improved Genetic Algorithm for Optimal Layout of Flow Meters and Valves in Water Network Partitioning

Shao

Yao

Zhang

et al. 2019

Water

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The paradigm of “divide and conquer” has been well used in Water Distribution Systems (WDSs) zoning planning in recent years. Indeed, Water Network Partitioning (WNP) has played an irreplaceable role in leakage control and pressure management; meanwhile it also has certain drawbacks, such as reduction of the supply reliability of the pipe network system and increased terminal dead water, as a result of the closure of the pipe section. In this paper, an improvement is made to the method proposed by Di Nardo et al. (2013) for optimal location of flow meters and valves. Three improvements to the genetic algorithm are proposed in this work for better and faster optimization in the dividing phase of WNP: preliminary hydraulic analysis which reduces the number of decision variables; modifications to the crossover mechanism to protect the superior individuals in the later stage; and boundary pipe grouping and mutation based on the pipe importance. The objective function considers the master–subordinate relationship when minimizing the number of flow meters and the difference of hydraulic state compared to original WDS. Another objective function of minimizing the deterioration of water quality compared to original WDS is also evaluated. The proposed method is applied for the WNP in a real WDS. Results show that it plays an effective role in the optimization of layout of the flow meters and valves in WNP.

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Section: Resultsmentioning

confidence: 99%

An Improved Genetic Algorithm for Optimal Layout of Flow Meters and Valves in Water Network Partitioning

Shao

Yao

Zhang

et al. 2019

Water

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“…Izquierdo et al [14] assessed anomalies utilizing a hybrid model composed of a deterministic part (flow rates and head at the nodes) coupled with a state estimation technique and artificial neural networks (ANN). Zhang et al [15] used the K-means algorithm to classify the water network into several zones and then used support vector machine (SVM) to locate the zones containing the leakages. Another approach suggested by Fang et al [16] is a prediction of leakage events with a convolutional neural network (CNN) dependent on historical data.…”

Section: Introductionmentioning

confidence: 99%

Water Leak Localization Using High-Resolution Pressure Sensors

Levinas¹,

Perelman²,

Ostfeld³

2021

Water

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

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“…These approaches can be further categorized into transient‐based methods, optimization‐based methods, and data‐driven methods. The transient‐based methods often analyze the transients induced by leaks using signal process techniques (e.g., Colombo et al, 2009; Wang et al, 2020); the optimization‐based methods typically define the leak detection as a demand calibration problem (e.g., Sanz et al, 2015; Sophocleous et al, 2019; Zhang et al, 2016); the data‐driven methods analyze the data from monitoring sites (e.g., pressure sensors and flow meters) to identify leaks (e.g., Romano et al, 2014; Zhou et al, 2019). While the model‐based approaches are efficient and cost‐effective in identifying leaks (Duan et al, 2011), they often exhibit equifinality and low‐reliability issues in practical applications, especially for highly looped and complex networks (Sophocleous et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Efficient Leak Localization in Water Distribution Systems Using Multistage Optimal Valve Operations and Smart Demand Metering

Huang

Zheng

Kapelan

et al. 2020

Water Resources Research

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This paper proposes a multistage method for burst leak localization through valve operations (VOs) and smart demand metering in district meter areas (DMAs) of water distribution systems (WDSs). Each stage includes partitioning of the DMA into two subregions using VOs and identification of potentially leaking pipes within the subregions through water balance analysis based on smart demand meters. Such a process is performed repeatedly (multiple stages) to narrow down the spatial range for pinpointing leak locations. To improve efficiency, a bisection optimization problem is formulated to localize the minimum leak areas using the lowest number of VOs, which is solved by a graph theory-based method. The utility of the proposed method is demonstrated using two DMAs (DMA1 and DMA2) of a real WDS with different topological properties. Results show that the proposed method can efficiently localize artificial burst leaks in DMA1 within 7-15% of the total pipe length, implying that the proposed method is theoretically effective in localizing pipe burst leaks. The real application to DMA2 has identified two leak regions with 2.3 and 4.2 km of pipe length (around 3-6% of the entire DMA2) using 18 VOs. These two burst leaks have been subsequently confirmed and pinpointed using listening rods by practitioners of the local water utility. These results indicate that the proposed multistage method is effective and efficient for burst leak localization, which can be promising for wide practical applications due to rapid developments of smart WDSs (e.g., smart demand meters or control valves).

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Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines

Cited by 118 publications

References 31 publications

An Improved Genetic Algorithm for Optimal Layout of Flow Meters and Valves in Water Network Partitioning

An Improved Genetic Algorithm for Optimal Layout of Flow Meters and Valves in Water Network Partitioning

Water Leak Localization Using High-Resolution Pressure Sensors

Efficient Leak Localization in Water Distribution Systems Using Multistage Optimal Valve Operations and Smart Demand Metering

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