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  • Pozycja
    Machine Learning for Water Leak Detection and Localization in the WaterPrime Project
    (Wydawnictwo Politechniki Łódzkiej, 2023) Głomb, Przemysław; Romaszewski, Michał; Cholewa, Michał; Koral, Wojciech; Madej, Andrzej; Skrabski, Maciej; Kołodziej, Katarzyna
    We 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.