Unsupervised anomaly detection in hourly water demand data using an asymmetric encoder–decoder model

Water demand forecasting (WDF) is essential for the design and optimal management of water distribution systems (WDS). Historical water demand data contribute significantly to WDF. Yet the obtained water demand data contain anomalies on occasions due to failures in WDS or monitoring systems. The con...

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Bibliographic Details

Published in:	Journal of hydrology (Amsterdam) Vol. 613; p. 128389
Main Authors:	Yan, Jieru, Tao, Tao
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.10.2022
Subjects:	Anomaly detection Asymmetric encoder–decoder Autoencoder forests Hourly water demand hybrids hydrology Time series water distribution water pollution Hourly water demand Asymmetric encoder–decoder Autoencoder Anomaly detection Time series
ISSN:	0022-1694, 1879-2707
Online Access:	Get full text
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