Machine learning‐based leakage identification in water distribution system

Abstract Ensuring access to clean water in urban areas is challenging due to leaks and contamination in complex water distribution systems (WDS). Traditional leak detection methods are slow, costly, time‐consuming and not very efficient, motivating the need for advanced solutions. Therefore, this study mainly focuses on Machine learning (ML)‐based leak detection method and localization by leveraging network data. Method uses emitter coefficients to simulate water loss and evaluates ML models, including K‐nearest neighbour (KNN), Random Forest and Support Vector Machine (SVM), on two example networks EPANET Network 3 and real‐life network of National Institute of Technology Kurukshetra campus. Random Forest achieved accuracy scores of 88.13% for Network 3 and 95.84% for the NIT network, with Area Under the Curve (AUC) scores of 0.87 and 0.98, respectively. The results highlight the model's effectiveness in detecting and localizing leaks, contributing to efficient water management. Further, the advantages and limitations of the study are discussed. The future study focuses on efficient applications of these models on real‐world problem.