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

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

doi:10.3390/smartcities4040069

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.

Highlights

Water leakage constitutes an important issue in managing water distribution systems because it causes economic losses, damages to the surrounding soil and infrastructures, and soil contamination
This paper presented an investigation of the use of machine learning methods to local leakage in the water distribution network
Leakage localization was based on the creation of hydraulic zones in the water distribution network

Summary

Introduction

Water leakage constitutes an important issue in managing water distribution systems because it causes economic losses, damages to the surrounding soil and infrastructures, and soil contamination. Water leaks can be detected by identifying soil voids created by water leaks or by detecting sections of pipes that appear deeper than they are due to the increase in the dielectric properties of the surrounding saturated soils. The free-swimming systems methods are based on introducing the water pipes of capsules with an embedded power source, electronic components, and instrumentation (acoustic sensor, accelerometer, magnetometer, GPS synchronized ultrasonic transmitter, and temperature sensor). These capsules record the internal environment of the pipes and send the recorded data to a server. This method is well adapted for pipes with large diameters

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