Another study introduced an innovative stacking ensemble model designed for contamination detection (Figure ) . The model uses various water quality parameters such as total chlorine, pH, electrical conductivity, water temperature, TOC, and turbidity. That model integrates multiple predictors into a meta-predictor, trained through cross-validation. That approach enhances the ability of the model to discern distinct features across water quality parameters. The ensemble has predictors such as ANN, SVM with a linear kernel, linear regressor, extra trees, uniform weighted KNN, and an RF meta-predictor. The ensemble demonstrates superior performance in detecting contamination compared with an ANN benchmark method, achieving higher accuracy, lower false positive rates, and improved F1-scores.