Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems

Mashhadi, Neda; Shahrour, Isam; Attoue, Nivine; El Khattabi, Jamal; Aljer, Ammar

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.3390/smartcities4040069

URL permanente :

http://hdl.handle.net/20.500.12210/125489

Titre :

Use of Machine Learning for Leak Detection and Localization in Water Distribution Systems

Auteur(s) :

Mashhadi, Neda [Auteur]
Shahrour, Isam [Auteur]

Laboratoire de Génie Civil et Géo-Environnement (LGCgE) - ULR 4515 [LGCgE]
Attoue, Nivine [Auteur]
Laboratoire de Génie Civil et Géo-Environnement (LGCgE) - ULR 4515 [LGCgE]
El Khattabi, Jamal [Auteur]
Laboratoire de Génie Civil et Géo-Environnement (LGCgE) - ULR 4515 [LGCgE]
Aljer, Ammar [Auteur]

Laboratoire de Génie Civil et Géo-Environnement (LGCgE) - ULR 4515 [LGCgE]

Titre de la revue :

Smart Cities

Pagination :

1293-1315

Éditeur :

MDPI

Date de publication :

2021-12

ISSN :

2624-6511

Discipline(s) HAL :

Environmental Sciences/Environment and Society

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :