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- W4372260538 abstract "Water is a valuable resource that has to be handled appropriately. However, a significant volume of water is wasted annually due to leaks in Water Distribution Networks (WDNs). This emphasizes the necessity for reliable and effective leak detection and localization systems. Several types of solutions have been proposed during the last few years. Among these solutions, data-driven ones are gaining more traction due to their impressive performance. In this paper, we propose a new method for leak detection and localization. The method is based on water pressure measurements acquired at a series of nodes of a WDN. Our technique is a fully data-driven solution that makes only use of the knowledge of the WDN topology, and a series of pressure data acquisitions obtained in absence of leaks. The proposed solution is based on an autoencoder trained on no-leak data, so that leaks are detected as anomalies. The results achieved on the LeakDB dataset demonstrate that the proposed solution outperforms recent methods for leak detection and localization." @default.
- W4372260538 created "2023-05-07" @default.
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- W4372260538 date "2023-06-04" @default.
- W4372260538 modified "2023-09-27" @default.
- W4372260538 title "Water Leak Detection and Localization Using Convolutional Autoencoders" @default.
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- W4372260538 doi "https://doi.org/10.1109/icassp49357.2023.10095760" @default.
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