Evaluating the Effectiveness of ML Algorithms in Water Potability Prediction Aligned with SDGs

This research addresses the critical need for accurate water quality prediction, aligning with Clean Water and Sanitation (SDG 6) and Good Health and Well-being (SDG 3). Utilizing a diverse dataset of water quality attributes, we developed and evaluated multiple machine learning models to predict water potability, contributing to SDG 9 (Industry, Innovation, and Infrastructure) through technological advancement. Our primary contribution is a Hyperparameter-Tuned Histogram Gradient Boosting Classifier, which outperformed several other algorithms, achieving an accuracy of 67%, a ROC-AUC score of 70%, and an F1-score of 66%. The study highlights challenges of class imbalance and feature distribution in water quality prediction, addressing aspects of SDG 11 (Sustainable Cities and Communities) by improving urban water management. Our findings have significant implications for water resource management, supporting SDG 12 (Responsible Consumption and Production) through efficient resource utilization. While acknowledging limitations such as single dataset reliance, this research combines modern machine learning with comprehensive analysis to enhance water quality assessment. It contributes to SDG 17 (Partnerships for the Goals) by providing a framework for collaboration between technology and environmental sectors. Future directions emphasize improving model performance and real-world applicability, potentially supporting SDG 13 (Climate Action) by enhancing resilience to water-related climate impacts.