A Sequential Pressure-Based Algorithm for Data-Driven Leakage Identification and Model-Based Localization in Water Distribution Networks

Leakages in water distribution networks (WDNs) are estimated to globally cost 39 billion USD/year and cause water and revenue losses, infrastructure degradation, and other cascading effects. Their impacts can be prevented and mitigated with prompt identification and accurate leak localization. In this work, we propose the leakage identification and localization algorithm (LILA), a pressure-based algorithm for data-driven leakage identification and model-based localization in WDNs. First, LILA identifies potential leakages via semisupervised linear regression of pairwise sensor pressure data and provides the location of their nearest sensors. Second, LILA locates leaky pipes relying on an initial set of candidate pipes and a simulation-based optimization framework with iterative linear and mixed-integer linear programming. LILA is tested on data from the L-Town network devised for the Battle of Leakage Detection and Isolation Methods. Results show that LILA can identify all leakages included in the data set and locate them within a maximum distance of 374 m from their real location. Abrupt leakages are identified immediately or within 2 h, while more time is required to raise alarms on incipient leakages. (C) 2022 American Society of Civil Engineers.

Automated summary

In this study, the leakage identification and localization algorithm (LILA) is proposed for water distribution networks. By analyzing sensor pressure data and using an optimization framework, LILA can accurately identify and locate leakages, with small localization errors.