Leak Detection and Localization in Water Distribution Networks via Online Change-Point Detection and Leak Sensitivity Modeling

Efficient leak detection and localization in water distribution networks (WDNs) are essential for mitigating disastrous losses from leak incidents. Current unsupervised learning-based methods excel in detecting single leaks but struggle with multi-leak scenarios, limiting their practical applicability. Additionally, leak localization methods relying on simulation optimization can be computationally intensive for large-scale WDNs. Meanwhile, purely data-driven approaches struggle to leverage network topology and hydraulic characteristics, hinde ring accurate leak localization. To address these challenges, we propose a leak detection and localization framework that employs online change-point detection and leak sensitivity modeling to achieve real-time detection and localization in multi-leak scenarios. For leak detection, the framework utilizes an unsupervised One-dimensional Convolutional Auto-encoder (1D-CAE) to reconstruct the pressure data collected from sensors deployed over a WDN. The residuals between the reconstructed values and the observed values, also known as reconstruction errors, are then analyzed using a Sequentially Discounting Normalized Maximum Likelihood (SDNML) change-point detector. Equipped with a customized scoring function designed for robust multi-leak detection, this detector computes anomaly scores in real-time at each time step, facilitating effective leak detection. Furthermore, we simulate leaks at different nodes of the WDN to quantify their impact on the sensor nodes using pressure residuals-based leak characteristic factors. By comparing reconstruction errors with these factors, we identify the nodes that best match the current leak event, achieving precise leak localization. An evaluation on the L-Town dataset demonstrates the superior performance of our method for multi-leak detection and localization.