Leak Detection in Smart Water Grids Using EPANET and Machine Learning Techniques

Abstract

Water supply networks are liable to leakages, resulting in loss of large quantities of water. Hence, it is required to implement a leak detection and localization system through water network simulations and IoT enabled sensors which will enable faster leak detection and repairs to leaks such as underground leaks, which would otherwise go undetected for long durations. This study presents a clear and detailed procedure to set up the water network and leak simulation in EPANET, to generate the dataset, to apply machine learning classifiers to predict leakage and to apply the proposed system to a real-world leak detection system. Using extended period analysis in EPANET, emitters have been used to model leakages of various sizes and at different locations, while keeping in mind the real-world constraints. Leaks cause changes in the dynamic network parameters such as pressure and flow. This fact has been utilized for leak prediction implementation. Once the simulated model is validated, the trained machine learning classifiers can be used to predict and localize the leaks in real time.