Leak detection and localization in water distribution networks by combining expert knowledge and data-driven models

Abstract

Leaks represent one of the most relevant faults in water distribution networks (WDN), resulting in severe losses. Despite the growing research interest in critical infrastructure monitoring, most of the solutions present in the literature cannot completely address the specific challenges characterizing WDNs, such as the low spatial resolution of measurements (flow and/or pressure recordings) and the scarcity of annotated data. We present a novel integrated solution that addresses these challenges and successfully detects and localizes leaks in WDNs. In particular, we detect leaks by a sequential monitoring algorithm that analyzes the inlet flow, and then we validate each detection by an ad hoc statistical test. We address leak localization as a classification problem, which we can simplify by a customized clustering scheme that gathers locations of the WDN where, due to the low number of sensors, it is not possible to accurately locate leaks. A relevant advantage of the proposed solution is that it exposes interpretable tuning parameters and can integrate knowledge from domain experts to cope with scarcity of annotated data. Experiments, performed on a real dataset of the Barcelona WDN with both real and simulated leaks, show that the proposed solution can improve the leak detection and localization performance with respect to methods proposed in the literature.