Explainable AI for Water Leakage Detection in Urban Water Distribution Networks Using Real and Simulated Data

Abstract

Water leakage in urban water distribution networks (WDNs) poses significant challenges for sustainable resource management and infrastructure reliability. Traditional detection methods are often reactive and difficult to scale in modern sensor-rich environments. This paper proposes a hybrid data-driven framework for early leak detection that integrates physics-informed simulation with machine learning and explainable analytics. A region-aware EPANET-style simulator is developed to generate realistic hydraulic data under varying demand patterns, environmental conditions, and pressure-dependent leak scenarios. To enhance generalizability, the synthetic dataset is combined with a BATADAL-inspired benchmark, enabling both in-domain and cross-domain evaluation. A feature engineering pipeline is introduced to capture temporal, spatial, and hydraulic relationships, expanding raw sensor signals into a high-dimensional representation. Six machine learning models, including Random Forest, Gradient Boosting, Support Vector Machine, Logistic Regression, Isolation Forest, and a PCA-Based Autoencoder, are systematically evaluated under constrained false-positive requirements. The results show that tree-based ensemble models achieve strong detection performance while maintaining low false-alarm rates (FPR ≤ 0.05). Importantly, cross-domain experiments demonstrate that models trained on simulated data retain competitive performance when applied to benchmark datasets, indicating robust transferability. Finally, explainability analysis reveals that pressure-based temporal statistics and spatial gradients are key indicators of leakage, providing interpretable insights for system monitoring. The proposed framework offers a scalable and generalizable approach for intelligent leak detection in modern water distribution systems.