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Leak localization in water distribution networks using GIS-Enhanced autoencoders
被引:1
作者:
Weyns, Michael
[1
]
Mazaev, Ganjour
[1
]
Vaes, Guido
[2
]
Vancoillie, Filip
[3
]
De Turck, Filip
[1
]
Van Hoecke, Sofie
[1
]
Ongenae, Femke
[1
]
机构:
[1] Ghent Univ imec, IDLab, Ghent, Belgium
[2] Hydroscan, Modelling & Res, Leuven, Belgium
[3] De Watergroep, Brussels, Belgium
来源:
关键词:
Leak localization;
water distribution networks;
machine learning;
autoencoders;
geographical information systems;
SYSTEMS;
D O I:
10.1080/1573062X.2023.2216191
中图分类号:
TV21 [水资源调查与水利规划];
学科分类号:
081501 ;
摘要:
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.
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页码:859 / 881
页数:23
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