Applying anomaly detection models in wastewater management: a case study of nitrates concentration in the effluent

Abstract

With an increase in the diversity of data that companies in our society produce today, extracting insights from them manually has become an arduous task. One of the processes of extracting knowledge from the data is the application of anomaly detection models, which allows for finding unusual patterns in a given dataset. The application of these models in the context of Wastewater Treatment Plants (WWTPs) can improve water quality monitoring in these facilities, alerting decision-makers to act more quickly and effectively on anomalous events. Hence, this study aims to conceive and evaluate several candidate models based on Isolations Forest and Long Short-Term Memory-Autoencoders (LSTM-AE) to detect anomalies in the WWTP effluent, namely in the concentration of nitrates. Considering the obtained results, the best candidate was the LSTM-AE-based model, which had the best performance with an F1-Score of 97% and an AUC-ROC of 98%.