Comparative Performance Analysis of RBF-Hybrid Artificial Neural Networks on Fault Detection in Wastewater Treatment Plants

The efficiency of wastewater treatment plants (WWTPs) highly depends on correctly detecting the anomalies hindering their processes. This study investigates the use of hybrid Artificial Neural Networks (A-NNs) for detecting mechanical faults injected in the Dissolved Oxygen (DO) sensor of a WWTP. The hybrid networks are obtained by combining Radial Basis Function Neural Network (RBF-NN) with the specific architectures of Feedforward Neural Network (FF-NN), Long Short-Term Memory Neural Network (LSTM-NN) and Convolutional Neural Network (C-NN), respectively. Each hybrid model is tested on several simulated anomaly scenarios containing both normal and faulty operating conditions of the DO sensor, and evaluated using a comprehensive set of classification metrics, including accuracy (A), precision (P), recall (R), F1-score (F1-S), balanced accuracy (BA), Cohen’s Kappa (CK), Matthew’s Correlation Coefficient (MCC), and the areas under the Receiver Operating Characteristic curve (ROC-AUC) and the Precision–Recall curve (PR-AUC). The results show that the LSTM-NN + RBF hybrid consistently outperforms the other two hybrids, achieving accuracy of 96.04%, precision of 96.78%, recall of 89.51%, F1-score of 92.89%, ROC-AUC of 96.01%, PR-AUC of 94.93%, MCC of 90.25%, CK of 90.03% and BA of 94.16%. These results suggest that the proposed LSTM-NN + RBF hybrid is a promising tool for efficiently detecting mechanical faults in a WWTP.