@inproceedings{2d8c6a3203a5438c83ee996d2d68a817,
title = "Risk assessment for early water leak detection",
abstract = "This paper presents the automation of the current leak detection operators experience and practice. A historical data driven statistical based approach is validated for real time leak detection in water distribution systems (WDS) yielding a color coded leak detection indicators (Likelihood LI, Severity SI, and Risk RI), which are integrated and visualized in an operator's user-friendly interface. This paper presents four signature scenarios using 3-year synthetic data from Eau de Paris monitored data. The data simulates leak scenarios in the WDS of the experimental demonstration site of Lille University Campus, France. The research includes: (1) assessment of the current state of practice; (2) development of an intelligent network control and on-site monitoring (INCOM) prototype system for early network leak detection; and (3) demonstration and assessment of the INCOM prototype system through off-line scenarios simulations. It supports the system operator(s) in {"}intelligent{"} real-time system monitoring and leak detection for preventive and proactive rather than reactive water distribution system management.",
author = "Cantos, {Wilmer P.} and Ilan Juran and Silvia Tinelli",
year = "2017",
doi = "10.1061/9780784481219.027",
language = "English (US)",
series = "International Conference on Sustainable Infrastructure 2017: Technology - Proceedings of the International Conference on Sustainable Infrastructure 2017",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "306--318",
editor = "Lucio Soibelman and Feniosky Pena-Mora",
booktitle = "International Conference on Sustainable Infrastructure 2017",
note = "2017 International Conference on Sustainable Infrastructure: Technology, ICSI 2017 ; Conference date: 26-10-2017 Through 28-10-2017",
}