Domain adaptation through active learning strategies for anomaly classification in wastewater treatment plants.

Abstract

The increasing use of intermittent aeration controllers in wastewater treatment plants (WWTPs) aims to reduce aeration costs via continuous ammonia and oxygen measurements but faces challenges in detecting sensor and process anomalies. Applying machine learning to this unbalanced, multivariate, multiclass classification challenge requires much data, difficult to obtain from a new plant. This study develops a machine learning algorithm to identify anomalies in intermittent aeration WWTPs, adaptable to new plants with limited data. Utilizing active learning, the method iteratively selects samples from the target domain to fine-tune a gradient-boosting model initially trained on data from 17 plants. Three sampling strategies were tested, with low probability and high entropy sampling proving effective in early adaptation, achieving an F2-score close to the optimal with minimal sample use. The objective is to deploy these models as decision support systems for WWTP management, providing a strategy for efficient model adaptation to new plants, and optimizing labeling efforts.