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

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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.

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Funding

This paper is supported by the key Science and technology project of the Education Department of Jilin Province (Grant No. JJKH20200983KJ), Natural Science Foundation of the Department of Science and Technology of Jilin Province (Grant No. 20200201046JC), and the key Science Foundation of the Department of Science and Technology of Jilin Province (Grant No. 20190303082SF). Thanks for the permission to publish this paper.

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Authors and Affiliations

  1. College of Communication Engineering, Jilin University, Changchun, China

    Juan Li & Wenjun Zheng

  2. College of Automotive Engineering, Jilin University, Changchun, China

    Changgang Lu

Authors
  1. Juan Li
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  2. Wenjun Zheng
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  3. Changgang Lu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by J Li, WJ Zheng, and CG Lu. The first draft of the manuscript was written by WJ Zheng and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Juan Li.

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Li, J., Zheng, W. & Lu, C. An Accurate Leakage Localization Method for Water Supply Network Based on Deep Learning Network. Water Resour Manage 36, 2309–2325 (2022). https://doi.org/10.1007/s11269-022-03144-x

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