Predicting water quality index using stacked ensemble regression and SHAP based explainable artificial intelligence.

ABSTRACT: Effective forecasting of the Water Quality Index (WQI) considerably impacts water resource management as well as public health safety. This study proposes a new approach for WQI forecasting using stacked regression ensemble modeling integrated with SHAP (Shapley Additive explanations), a form of Explainable Artificial Intelligence (XAI). The model was developed using a dataset of 1,987 water quality samples from Indian rivers (2005-2014), processed through six optimized machine learning algorithms: XGBoost, CatBoost, Random Forest, Gradient Boosting, Extra Trees, and AdaBoost, combined using Linear Regression as the meta-learner. The model was trained using seven normalized physicochemical parameters as predictors, and the computed WQI (via the weighted arithmetic method) served as the res

SUBMITTER: Choudhary R 

PROVIDER: S-EPMC12375761 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature