Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data

TL;DR

This paper introduces a deep autoencoder based on 1D CNNs for detecting anomalies in multivariate wastewater monitoring data, aiding experts in identifying abnormal patterns automatically.

Contribution

It presents a novel CNN-based autoencoder architecture tailored for in-situ wastewater data anomaly detection, emphasizing reconstruction error analysis.

Findings

Effective detection of anomalies in wastewater sensor data

Supports domain experts in identifying abnormal behaviors

Addresses challenges in labeling complex time series

Abstract

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.