An Accurate Leakage Localization Method for Water Supply Network Based on Deep Learning Network

Juan Li (), Wenjun Zheng () and Changgang Lu ()
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Juan Li: Jilin University
Wenjun Zheng: Jilin University
Changgang Lu: Jilin University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 7, No 11, 2309-2325

Abstract: Abstract In the water supply network, leakage of pipes will cause water loss and increase the risk of environmental pollution. For water supply systems, identifying the leak point can improve the efficiency of pipeline leak repair. Most existing leak location methods can only locate the leak point approximately at the node or pipe section of the pipe network but cannot locate the specific location of the pipe section. This paper presents a framework for accurate water supply network leakage location based on Residual Network (ResNet). This study proposes a leak localization idea with a parallel classification and regression process that enables the framework to pinpoint the exact position of leak points in the pipeline. Furthermore, a multi-supervision mechanism is designed in the regression process to speed up the model’s convergence. For a pipe network containing 40 pipes, the positioning accuracy of the pipe section is 0.94, and the MSE of the specific location of the leakage point is 0.000435. For the pipe network containing 117 pipes, the positioning accuracy of the pipe section is 0.91. The MSE of the specific location of the leakage point is 0.0009177. Experiments confirm the robustness and applicability of the framework.

Keywords: Leakage localization; Water distribution network; Deep learning; ResNet; Water loss management (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11269-022-03144-x

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