Application of explainable artificial intelligence and machine learning in wastewater treatment plants variable prediction: A comparative study of small and large-scale treatment plants

Abstract

Explainable artificial intelligence (XAI) can play a significant role in the application of machine learning (ML) in wastewater treatment plants (WWTPs). The present research focuses on evaluating the performance and generalizability of widely used ML models for predicting key effluent quality variables at multiple WWTPs. Effluent variables including ammonia nitrogen (NH₃-N), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), and total suspended solids (TSS) were predicted using eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. Several feature selections (FS) and XAI tools were used to understand the influence of input variables on target variables and impact of input variables on model performance. The study demonstrates how XAI can enhance the understanding of model behavior by identifying key input variables, thereby supporting more informed and transparent decision-making at WWTPs. The study finds that XAI methods are effective in capturing influence of variables regardless of the choice of model for variable prediction. XAI tools, SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), are successful in providing deeper understanding of the key factors influencing ML model performance. The findings of this research will facilitate WWTP operation with better decision-making in choosing ML models to optimize treatment performance and improve environmental sustainability.