Głomb, PrzemysławRomaszewski, MichałCholewa, MichałKoral, WojciechMadej, AndrzejSkrabski, MaciejKołodziej, Katarzyna2023-09-222023-09-222023Głomb P., Romaszewski M., Cholewa M., Koral W., Madej A., Skrabski M., Kołodziej K., Machine Learning for Water Leak Detection and Localization in the WaterPrime Project. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 193-194, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.30.978-83-66741-92-8http://hdl.handle.net/11652/4806https://doi.org/10.34658/9788366741928.30We present an integrated approach for water leak detection and localization developed for the WaterPrime project. Proposed method is based on telemetric monitoring of a District Metered Areas (DMA), using first an application of anomaly detection on sensors’ data and then building a ‘digital twin’ of a DMA state using a combination of hydraulic simulator and machine learning algorithms. This approach leads to reduction of time of leak location estimation from the order of weeks/months to days, and provides a significant reduction in quantity of water lost, as was preliminary verified in two waterworks associated with the project.enDla wszystkich w zakresie dozwolonego użytkuFair use conditionleak detectionleak localizationanomaly detection in time seriesmachine learning of a digital twindetekcja wyciekówlokalizacja wyciekówwykrywanie anomalii w szeregach czasowychuczenie maszynowe cyfrowego bliźniakaRozdział - monografiaLicencja PŁLUT License10.34658/9788366741928.30