Graph neural network for integrated water network partitioning and dynamic district metered areas

被引：0
作者：
Minglei Fu
Kezhen Rong
Yangyang Huang
Ming Zhang
Lejing Zheng
Jianfeng Zheng
Mayadah W. Falah
Zaher Mundher Yaseen
机构：
[1] Zhejiang University of Technology,College of Information Engineering
[2] Hangzhou Laison Technology Co.,Department of Building and Construction Technologies Engineering
[3] Ltd,New Era and Development in Civil Engineering Research Group, Scientific Research Center
[4] AL-Mustaqbal University College,undefined
[5] Al-Ayen University,undefined
来源：
Scientific Reports | / 12卷
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摘要：
Water distribution systems (WDSs) are used to transmit and distribute water resources in cities. Water distribution networks (WDNs) are partitioned into district metered areas (DMAs) by water network partitioning (WNP), which can be used for leak control, pollution monitoring, and pressure optimization in WDS management. In order to overcome the limitations of optimal search range and the decrease of recovery ability caused by two-step WNP and fixed DMAs in previous studies, this study developed a new method combining a graph neural network to realize integrated WNP and dynamic DMAs to optimize WDS management and respond to emergencies. The proposed method was tested in a practical case study; the results showed that good hydraulic performance of the WDN was maintained and that dynamic DMAs demonstrated excellent stability in emergency situations, which proves the effectiveness of the method in WNP.
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