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

Jieru Yan; Tao Tao

doi:10.1016/j.jhydrol.2022.128389

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

Jieru Yan Tao Tao

Year: 2022 Journal: Journal of Hydrology Vol: 613 Pages: 128389-128389 Publisher: Elsevier BV

DOI: 10.1016/j.jhydrol.2022.128389

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Keywords:

Anomaly detection Autoencoder Computer science Outlier Anomaly (physics) SIGNAL (programming language) Upstream (networking) Data mining Feature (linguistics) Pattern recognition (psychology) Environmental science Artificial intelligence Deep learning Telecommunications

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Water Systems and Optimization

Physical Sciences → Engineering → Civil and Structural Engineering

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Hydrology and Drought Analysis

Physical Sciences → Environmental Science → Global and Planetary Change

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

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