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Graph convolutional networks based contamination source identification across water distribution networks
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Zhou, Yujue, Jiang, Jie, Qian, Kai, Ding, Yulong, Yang, Shuang-Hua and He, Ligang (2021) Graph convolutional networks based contamination source identification across water distribution networks. Process Safety and Environmental Protection, 155 . pp. 317-324. doi:10.1016/j.psep.2021.09.008 ISSN 0957-5820.
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WRAP-Graph-convolutional-contamination-identification-water-distribution-networks-2021.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1576Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.psep.2021.09.008
Abstract
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.
| Item Type: | Journal Article | ||||||||||||||||||
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| Subjects: | Q Science > QA Mathematics T Technology > TD Environmental technology. Sanitary engineering |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | Water -- Distribution, Convolutions (Mathematics), Water -- Pollution | ||||||||||||||||||
| Journal or Publication Title: | Process Safety and Environmental Protection | ||||||||||||||||||
| Publisher: | Elsevier | ||||||||||||||||||
| ISSN: | 0957-5820 | ||||||||||||||||||
| Official Date: | November 2021 | ||||||||||||||||||
| Dates: |
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| Volume: | 155 | ||||||||||||||||||
| Page Range: | pp. 317-324 | ||||||||||||||||||
| DOI: | 10.1016/j.psep.2021.09.008 | ||||||||||||||||||
| Status: | Peer Reviewed | ||||||||||||||||||
| Publication Status: | Published | ||||||||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||||||||
| Date of first compliant deposit: | 22 October 2021 | ||||||||||||||||||
| Date of first compliant Open Access: | 10 September 2022 | ||||||||||||||||||
| RIOXX Funder/Project Grant: |
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