Graph convolutional networks based contamination source identification across water distribution networks

Zhou，Yujue1,2; Jiang，Jie2; Qian，Kai2; Ding，Yulong2,4; Yang，Shuang Hua2,3; He，Ligang1

2021-11-01

10.1016/j.psep.2021.09.008

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION   影响因子和分区

0957-5820

Water distribution Networks (WDNs) are one of the most important infrastructures for modern society. Due to accidental or malicious reasons, water contamination incidents have been repeatedly reported all over the world, which not only disrupt the water supply but also endanger public health. To ensure the safety of WDNs, water quality sensors are deployed across the WDNs for real-time contamination detection and source identification. In the literature, various methods have been employed to improve the performance of contamination source identification (CSI) and recent studies show that there is a great potential to tackle the CSI problem by deep learning models. The success of deep learning based CSI methods often requires a large size of training samples being collected. In real-world situations, the number of contamination events occurring in a single WDN is rather small, especially for a newly built WDN. However, the existing CSI methods in the literature mostly focus on the study of training and applying models on the same WDNs and the knowledge of CSI gained from one WDN cannot be reused by a different WDN. To these ends, based on the application of graph convolutional networks, this paper provides a solution for cross-network CSI that can transfer the CSI knowledge learned from one WDN to a different WDN. Empirically, based on a benchmark WDN in the task of contamination source identification, we show that the proposed cross-network CSI method can achieve comparable accuracy even trained on a different WDN.

Contamination source identification Cross-networks learning Graph convolutional network Water distribution networks

SCI ; EI

英语

通讯

WOS:000623811400005

20214010972382

Contamination ; Convolution ; Deep learning ; Water pollution ; Water quality ; Water supply

Water Analysis:445.2 ; Water Supply Systems:446.1 ; Water Pollution:453 ; Ergonomics and Human Factors Engineering:461.4 ; Information Theory and Signal Processing:716.1

ENVIRONMENT/ECOLOGY

2-s2.0-85115991403

Scopus

1.Department of Computer Science,University of Warwick,Coventry,CV4 7AL,United Kingdom
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,1088 Xueyuan Avenue,518055,China
3.Peng Cheng Laboratory,Shenzhen,No. 2, Xingke 1st Street,518066,China
4.Academy for Advanced Interdisciplinary Studies,Southern University of Science and Technology,Shenzhen,1088 Xueyuan Avenue,518055,China

Zhou，Yujue,Jiang，Jie,Qian，Kai,et al. Graph convolutional networks based contamination source identification across water distribution networks[J]. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION,2021,155:317-324.

Zhou，Yujue,Jiang，Jie,Qian，Kai,Ding，Yulong,Yang，Shuang Hua,&He，Ligang.(2021).Graph convolutional networks based contamination source identification across water distribution networks.PROCESS SAFETY AND ENVIRONMENTAL PROTECTION,155,317-324.

Zhou，Yujue,et al."Graph convolutional networks based contamination source identification across water distribution networks".PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 155(2021):317-324.