Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data
This work addresses the need for high data quality in wastewater systems for domain experts, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of automatically identifying abnormal behaviors in wastewater systems monitoring data by proposing a deep autoencoder-based anomaly detection method, achieving validation on multivariate time series from in-sewer process monitoring data.
Due to the growing amount of data from in-situ sensors in wastewater systems, it becomes necessary to automatically identify abnormal behaviours and ensure high data quality. This paper proposes an anomaly detection method based on a deep autoencoder for in-situ wastewater systems monitoring data. The autoencoder architecture is based on 1D Convolutional Neural Network (CNN) layers where the convolutions are performed over the inputs across the temporal axis of the data. Anomaly detection is then performed based on the reconstruction error of the decoding stage. The approach is validated on multivariate time series from in-sewer process monitoring data. We discuss the results and the challenge of labelling anomalies in complex time series. We suggest that our proposed approach can support the domain experts in the identification of anomalies.