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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Vydané v:	Journal of hydrology (Amsterdam) Ročník 613; s. 128389
Hlavní autori:	Yan, Jieru, Tao, Tao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.10.2022
Predmet:	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
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