A decision support machine learning tool for environmental bioremediation as water safety in India

This study presents a novel, interpretable Decision Support System that integrates Stacking Ensemble learning with Shapley Additive exPlanations to predict water potability and recommend bioremediation strategies. Preparation and validation based on Indian datasets with over 4000 samples was carried out and the proposed Stacking Ensemble (RF, XGBoost, SVM, KNN, LR) achieved the highest accuracy of 74.0%, with Area under the Curve of 0.806, being at about ten percent above other classifiers, and maintaining state-of-the-art level standards. Further beyond prediction, the framework locates significant contamination drivers (such as pH and Sulfates) and maps them to viable ecological treatments such as phytoremediation and microbial degradation, which recent studies lack. This study demonstrates a scalable, transparent AI-driven pathway for real-time water quality management in resource-constrained environments.