Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems
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-tim...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/4/4/69 |
| _version_ | 1797500593769545728 |
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| author | Neda Mashhadi Isam Shahrour Nivine Attoue Jamal El Khattabi Ammar Aljer |
| author_facet | Neda Mashhadi Isam Shahrour Nivine Attoue Jamal El Khattabi Ammar Aljer |
| author_sort | Neda Mashhadi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:06:03Z |
| format | Article |
| id | doaj.art-b1a8418a8c6e4ee2b35c7f85fae7cd46 |
| institution | Directory Open Access Journal |
| issn | 2624-6511 |
| language | English |
| last_indexed | 2024-03-10T03:06:03Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj.art-b1a8418a8c6e4ee2b35c7f85fae7cd462023-11-23T10:33:17ZengMDPI AGSmart Cities2624-65112021-10-01441293131510.3390/smartcities4040069Use of Machine Learning for Leak Detection and Localization in Water Distribution SystemsNeda Mashhadi0Isam Shahrour1Nivine Attoue2Jamal El Khattabi3Ammar Aljer4Civil and Geo-Environmental Engineering Laboratory (LGCgE), Lille University, 5900 Lille, FranceCivil and Geo-Environmental Engineering Laboratory (LGCgE), Lille University, 5900 Lille, FranceCivil and Geo-Environmental Engineering Laboratory (LGCgE), Lille University, 5900 Lille, FranceCivil and Geo-Environmental Engineering Laboratory (LGCgE), Lille University, 5900 Lille, FranceCivil and Geo-Environmental Engineering Laboratory (LGCgE), Lille University, 5900 Lille, FranceThis 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.https://www.mdpi.com/2624-6511/4/4/69EPANETflowlocalizationmachine learningpressureleak |
| spellingShingle | Neda Mashhadi Isam Shahrour Nivine Attoue Jamal El Khattabi Ammar Aljer Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems Smart Cities EPANET flow localization machine learning pressure leak |
| title | Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems |
| title_full | Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems |
| title_fullStr | Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems |
| title_full_unstemmed | Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems |
| title_short | Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems |
| title_sort | use of machine learning for leak detection and localization in water distribution systems |
| topic | EPANET flow localization machine learning pressure leak |
| url | https://www.mdpi.com/2624-6511/4/4/69 |
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