Hybrid Transient-Machine Learning Methodology for Leak Detection in Water Transmission Mains
This contribution proposes a hybrid approach integrating transient test-based techniques with machine learning for automatic leak detection in water transmission mains. Transient numerical simulations calibrated using experimental tests are used to develop a data-driven method based on neural networ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/69/1/142 |
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| author | Caterina Capponi Andrea Menapace Silvia Meniconi Daniele Dalla Torre Maurizio Tavelli Maurizio Righetti Bruno Brunone |
| author_facet | Caterina Capponi Andrea Menapace Silvia Meniconi Daniele Dalla Torre Maurizio Tavelli Maurizio Righetti Bruno Brunone |
| author_sort | Caterina Capponi |
| collection | DOAJ |
| description | This contribution proposes a hybrid approach integrating transient test-based techniques with machine learning for automatic leak detection in water transmission mains. Transient numerical simulations calibrated using experimental tests are used to develop a data-driven method based on neural networks to identify leak locations and characteristics. The accuracy of leak localization is demonstrated using three different degrees of noise in terms of mean absolute error, ranging between 0.54 m and 2.1 m. This proposed hybrid approach shows prospects for in-field applications. |
| format | Article |
| id | doaj-art-6e8b52d9dfb14da0955a4d071c572abe |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-6e8b52d9dfb14da0955a4d071c572abe2025-08-20T02:11:13ZengMDPI AGEngineering Proceedings2673-45912024-09-0169114210.3390/engproc2024069142Hybrid Transient-Machine Learning Methodology for Leak Detection in Water Transmission MainsCaterina Capponi0Andrea Menapace1Silvia Meniconi2Daniele Dalla Torre3Maurizio Tavelli4Maurizio Righetti5Bruno Brunone6Department of Civil and Environmental Engineering, University of Perugia, 06125 Perugia, ItalyFaculty of Agricultural, Environmental and Food Science, Free University of Bolzano/Bozen, 39100 Bolzano, ItalyDepartment of Civil and Environmental Engineering, University of Perugia, 06125 Perugia, ItalyFaculty of Agricultural, Environmental and Food Science, Free University of Bolzano/Bozen, 39100 Bolzano, ItalyFaculty of Engineering, Free University of Bolzano/Bozen, 39100 Bolzano, ItalyFaculty of Agricultural, Environmental and Food Science, Free University of Bolzano/Bozen, 39100 Bolzano, ItalyDepartment of Civil and Environmental Engineering, University of Perugia, 06125 Perugia, ItalyThis contribution proposes a hybrid approach integrating transient test-based techniques with machine learning for automatic leak detection in water transmission mains. Transient numerical simulations calibrated using experimental tests are used to develop a data-driven method based on neural networks to identify leak locations and characteristics. The accuracy of leak localization is demonstrated using three different degrees of noise in terms of mean absolute error, ranging between 0.54 m and 2.1 m. This proposed hybrid approach shows prospects for in-field applications.https://www.mdpi.com/2673-4591/69/1/142transmission mainspressure transientanomaly detectionexperimental testsmachine learning |
| spellingShingle | Caterina Capponi Andrea Menapace Silvia Meniconi Daniele Dalla Torre Maurizio Tavelli Maurizio Righetti Bruno Brunone Hybrid Transient-Machine Learning Methodology for Leak Detection in Water Transmission Mains Engineering Proceedings transmission mains pressure transient anomaly detection experimental tests machine learning |
| title | Hybrid Transient-Machine Learning Methodology for Leak Detection in Water Transmission Mains |
| title_full | Hybrid Transient-Machine Learning Methodology for Leak Detection in Water Transmission Mains |
| title_fullStr | Hybrid Transient-Machine Learning Methodology for Leak Detection in Water Transmission Mains |
| title_full_unstemmed | Hybrid Transient-Machine Learning Methodology for Leak Detection in Water Transmission Mains |
| title_short | Hybrid Transient-Machine Learning Methodology for Leak Detection in Water Transmission Mains |
| title_sort | hybrid transient machine learning methodology for leak detection in water transmission mains |
| topic | transmission mains pressure transient anomaly detection experimental tests machine learning |
| url | https://www.mdpi.com/2673-4591/69/1/142 |
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