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Sensor Partitioning Placements via Random Walk and Water Quality and Leakage Detection Models within Water Distribution Systems

Author

Listed:
  • Tianwei Mu

    (Shenyang University, Ministry of Education)

  • Manhong Huang

    (State Environmental Protection Engineering Center for Pollution Treatment and Control in Textile Industry, Donghua University)

  • Shi Tang

    (Tieling Construction Engineering Construction Drawing Examination Co, Ltd.
    Shenyang Jianzhu University)

  • Rui Zhang

    (Dalian University of Technology)

  • Gang Chen

    (State Environmental Protection Engineering Center for Pollution Treatment and Control in Textile Industry, Donghua University)

  • Baiyi Jiang

    (Shenyang Jianzhu University)

Abstract

A novel sensor partitioning placement model is presented to evenly distribute sensors to water distribution systems (WDS) for monitoring leakages and contamination. First, random walk community detection (RWCD) is used to divide WDS into different partitions. Then, an extended period leakage detection (EPLD) model is presented. The total leakage detection and the average time of leakage detection are used as objective functions for pressure sensor placement. Next, the extended period water quality detection (EPWQD) model is presented. The total intrusion detection, the average percentage of clean water, and the average time of water quality detection are used as objective functions for water quality sensor placement. Evolutionary algorithm (EA) modules are applied to optimize the locations of pressure and water quality sensors. Seven networks are employed to verify the practicability of the model. The results show that leakage and intrusion detection rate is up to 85% during 24 h, and the average percentage of clean water is up to 0.9 in these cases. Finally, the model compares the leakage zone identification (LZI) and the water quality sensor placement strategy (WQSPS) models. The total detection number, the total average time of detection, and the total average percentage of clean water have been improved. Therefore, this model is a high-potential way of sensor placement. Graphical Abstract

Suggested Citation

  • Tianwei Mu & Manhong Huang & Shi Tang & Rui Zhang & Gang Chen & Baiyi Jiang, 2022. "Sensor Partitioning Placements via Random Walk and Water Quality and Leakage Detection Models within Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5297-5311, October.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:13:d:10.1007_s11269-022-03312-z
    DOI: 10.1007/s11269-022-03312-z
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    References listed on IDEAS

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    1. LAMBIOTTE, Renaud & DELVENNE, Jean-Charles & BARAHONA, Mauricio, 2014. "Random walks, Markov processes and the multiscale modular organization of complex network," LIDAM Reprints CORE 2660, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Tianwei Mu & Yan Lu & Haoqiang Tan & Haowen Zhang & Chengzhi Zheng, 2021. "Random Walks Partitioning and Network Reliability Assessing in Water Distribution System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(8), pages 2325-2341, June.
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