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Pressure Sensor Placement in Water Supply Network Based on Graph Neural Network Clustering Method

School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China
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Academic Editor: Francesco De Paola
Water 2022, 14(2), 150; https://doi.org/10.3390/w14020150
Received: 26 November 2021 / Revised: 30 December 2021 / Accepted: 5 January 2022 / Published: 7 January 2022
(This article belongs to the Section Urban Water Management)
Pressure sensor placement is critical to system safety and operation optimization of water supply networks (WSNs). The majority of existing studies focuses on sensitivity or burst identification ability of monitoring systems based on certain specific operating conditions of WSNs, while nodal connectivity or long-term hydraulic fluctuation is not fully considered and analyzed. A new method of pressure sensor placement is proposed in this paper based on Graph Neural Networks. The method mainly consists of two steps: monitoring partition establishment and sensor placement. (1) Structural Deep Clustering Network algorithm is used for clustering analysis with the integration of complicated topological and hydraulic characteristics, and a WSN is divided into several monitoring partitions. (2) Then, sensor placement is carried out based on burst identification analysis, a quantitative metric named “indicator tensor” is developed to calculate hydraulic characteristics in time series, and the node with the maximum average partition perception rate is selected as the sensor in each monitoring partition. The results showed that the proposed method achieved a better monitoring scheme with more balanced distribution of sensors and higher coverage rate for pipe burst detection. This paper offers a new robust framework, which can be easily applied in the decision-making process of monitoring system establishment. View Full-Text
Keywords: pressure sensor placement; water supply network; graph neural network; structural deep clustering network; monitoring system; pipe burst identification pressure sensor placement; water supply network; graph neural network; structural deep clustering network; monitoring system; pipe burst identification
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MDPI and ACS Style

Peng, S.; Cheng, J.; Wu, X.; Fang, X.; Wu, Q. Pressure Sensor Placement in Water Supply Network Based on Graph Neural Network Clustering Method. Water 2022, 14, 150. https://doi.org/10.3390/w14020150

AMA Style

Peng S, Cheng J, Wu X, Fang X, Wu Q. Pressure Sensor Placement in Water Supply Network Based on Graph Neural Network Clustering Method. Water. 2022; 14(2):150. https://doi.org/10.3390/w14020150

Chicago/Turabian Style

Peng, Sen, Jing Cheng, Xingqi Wu, Xu Fang, and Qing Wu. 2022. "Pressure Sensor Placement in Water Supply Network Based on Graph Neural Network Clustering Method" Water 14, no. 2: 150. https://doi.org/10.3390/w14020150

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