01376nas a2200181   4500000000100000008004100001260001200042100002300054700002000077700001900097700001800116700002400134245006600158856004000224490000700264520090900271022001401180       2023                            d  c05/20231 aPrzemysław Głomb1 aMichał Cholewa1 aWojciech Koral1 aAndrzej Madej1 aMichał Romaszewski00aDetection of emergent leaks using machine learning approaches  uhttps://doi.org/10.2166/ws.2023.1180 v233 a<p>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.</p>
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