Ensemble Machine Learning for Reliable Water Potability Prediction with Optimized Physicochemical Feature Engineering - Srivastava, Rohit; Sinha, Keshav; Samanta, Dheeraj; Alsabet, Reham; El-Shafai, Walid; Azar, Ahmad Taher (Engineering, Technology & Applied Science Research, 2026)
Access to safe drinking water is a global challenge for both health and sustainability. This study presents a comprehensive examination of supervised machine learning models for water potability classification using physicochemical water quality characteristics, and a leakage-safe preprocessing pipeline incorporating stratified data splitting, mean imputation, and feature standardization. Class imbalance is handled by the Synthetic Minority Over-Sampling Technique (SMOTE), and feature selection and normalization were used to preprocess the dataset. Grid search and 5-fold cross-validation were used to train and optimize models, namely Random Forest (RFC), Logistic Regression (LR), Decision Tree (DTC), AdaBoost, K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), and Gradient Boosting (GBC). Accuracy, precision, recall, and F1 score on the test set were used to assess performance, showing that ensemble approaches, namely RFC and GBC, performed better, having outstanding stability and prediction accuracy. Preprocessing contributes to increasing model sensitivity to the minority class, while balancing trade-offs between interpretability and model complexity are discussed. The findings contribute to the development of reliable, data-driven water quality monitoring systems that can align with sustainable development and public health goals. Since the limited dataset size may affect the generalizability to other regions, future studies should validate these findings on more diverse and comprehensive datasets.