Detection of emergent leaks using machine learning approaches - P. Głomb; M. Cholewa; W. Koral; A. Madej; M. Romaszewski (Water Supply, 2023)
In this work, we focus on the detection of leaks occurring in district metered areas (DMAs). Those leaks are observable as a number of time-related deviations from zone patterns over days or weeks. While they are detectable given enough time, due to the huge cost of water loss resulting from an undetected leak, the main challenge is to find them as soon as possible, when the deviation from the zone pattern is small. Using our collected observational data, we investigate the appearance of leaks and discuss the performance of several machine learning (ML) anomaly detectors in detecting them. We test a diverse set of six anomaly detectors, each based on a different ML algorithm, on nine scenarios containing leaks and anomalies of various kinds. The proposed approach is very effective at quickly (within hours) identifying the presence of a leak, with a limited number of false positives. HIGHLIGHTS We focus on the detection of leaks and anomalies occurring in the district metered areas (DMAs).; We use machine learning anomaly detection algorithms on hourly inflow, loss, consumption and pressure data.; We test the proposed approach on nine scenarios and show its good performance, potentially finding leaks within hours, with a limited number of false positives.;