Abstract
Ensuring safe drinking water is crucial for public health, yet existing assessment methods often fail to capture complex contaminant interactions, provide real-time analysis, or offer model interpretability. Traditional threshold-based evaluations lack adaptability, while many machine learning models are opaque, making it difficult to understand key influencing factors. To address these challenges, we propose SafeH2O, a data-driven framework integrating machine learning and explainability techniques. The system applies data preprocessing, including normalization, feature scaling, and class balancing using SMOTE, to enhance dataset reliability. K-means clustering is employed to categorize water samples, followed by supervised classifiers—such as Random Forest, Logistic Regression, and Gradient Boosting—to predict water potability. Among them, Gradient Boosting achieves the highest accuracy of 93%. To improve interpretability, local interpretable model-agnostic explanations (LIME) is used to analyze key contaminants influencing water quality across clusters. Additionally, we design a health risk assessment microservice that issues real-time contamination alerts and safe water recommendations.
