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.

Wasserstein-Enabled Leaks Localization in Water Distribution Networks

“…Similar, Ref. [21] simultaneously address leak detection and localization problems, with leaks detected and validated by statistically analyzing the inlet flow. Localization is formulated as a classification problem, and computational complexity is mitigated through a clustering scheme.…”

Real-Time Leak Diagnosis in Water Distribution Systems Based on a Bank of Observers and a Genetic Algorithm

“…If a leak occurs, an alarm is triggered when |Q l | > δ. Other kind of alarms can be found in [27]. At this point, the GA-based branch identification algorithm following the principles presented in Section 2.4 is started.…”

Topological analysis of water distribution networks for optimal leak localization

“…Thinking about these challenges, recent studies have proposed analyzing the problem using datadriven methods [3,4] that combine the use of standard operation data and topological information. The method in [5] studies the effect of the extra flow when a leak occurs in the pressure sensors presented in the network.…”

“…This work presents two new methodologies for sensor placement in the WDN, one using hydraulic simulation formulated as an integer optimization problem solved with a Genetic Algorithm (GA). And the other uses only the topological network information to improve the leak localization methods that use residual analysis [1,3,5]. The second approach aspires to make easier the sensor placement problem by suppressing the calibration of the hydraulic water models and reducing the computational burden.…”