ID:
publications-3869
Type:
article
Year:
2021
Authors:
Mashhadi, Neda and Mashhadi, Neda and Shahrour, Isam and Shahrour, Isam and Attoue, Nivine and Attoue, Nivine and Khattabi, Jamal El and Khattabi, Jamal El and Aljer, Ammar and Aljer, Ammar
Title:
Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems
Venue/Journal:
DOI:
10.3390/smartcities4040069
Research type:
Water System:
Technical Focus:
Abstract:
This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters.
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