Comparative Analysis of Machine Learning Models and Explainable Artificial Intelligence for Predicting Wastewater Treatment Plant Variables

Increasing urban wastewater and rigorous discharge regulations pose significant challenges for wastewater treatment plants (WWTP) to meet regulatory compliance while minimizing operational costs. This study explores the application of several machine learning (ML) models specifically, Artificial Neural Networks (ANN), Gradient Boosting Machines (GBM), Random Forests (RF), eXtreme Gradient Boosting (XGBoost), and hybrid RF-GBM models in predicting important WWTP variables such as Biochemical Oxygen Demand (BOD), Total Suspended Solids (TSS), Ammonia (NH₃), and Phosphorus (P). Several feature selection (FS) methods were employed to identify the most influential WWTP variables. To enhance ML models’ interpretability and to understand the impact of variables on prediction, two widely used explainable artificial intelligence (XAI) methods-Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) were investigated in the study. Results derived from FS and XAI methods were compared to explore their reliability. The ML model performance results revealed that ANN, GBM, XGBoost, and RF-GBM have great potential for variable prediction with low error rates and strong correlation coefficients such as R² value of 1 on the training set and 0.98 on the test set. The study also revealed that XAI methods identify common influential variables in each model’s prediction. This is a novel attempt to get an overview of both LIME and SHAP explanations on ML models for a WWTP variable prediction.