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

Tianwei Mu (), Manhong Huang, Shi Tang, Rui Zhang, Gang Chen and Baiyi Jiang
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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.
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

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2022, vol. 36, issue 13, No 23, 5297-5311

Abstract: 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

Keywords: Pressure sensor; Water quality sensor; Network partition; Multiple objective optimizations (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11269-022-03312-z

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