Leak localization in water distribution networks using GIS-Enhanced autoencoders

Autor:	Michael Weyns, Ganjour Mazaev, Guido Vaes, Filip Vancoillie, Filip De Turck, Sofie Van Hoecke, Femke Ongenae
Rok vydání:	2023
Předmět:	geographical information systems machine learning water distribution networks SYSTEMS autoencoders Geography Planning and Development Leak localization Water Science and Technology
Zdroj:	URBAN WATER JOURNAL
ISSN:	1744-9006 1573-062X
DOI:	10.1080/1573062x.2023.2216191
Popis:	Water loss due to persistent leakages in water distribution networks remains a substantive problem around the world, all the more so given noticeable trends of increasing global water scarcities. In this paper, we present a data-driven leak localization approach leveraging a connected Geographical Information System together with an autoencoder to perform anomaly detection on time-variable sensor data. Data-driven approaches are able to circumvent many of the uncertainty issues associated with model-based approaches, but they usually require significant amounts of high-quality data, reflecting many different leak scenarios, to perform well. Our approach obviates this requirement by relying only on leakless data during model training. We examine the efficacy of this approach on 19 realistic leak experiments conducted in the field. Based on these evaluations, we were able to achieve average search costs as low as 2.2 kilometers, for a total network length of 215 kilometers.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::15fe2f89d41e22e9a6ead80aef8968cd https://doi.org/10.1080/1573062x.2023.2216191 Zobrazit plný text záznamu