Access to safe drinking water is a fundamental determinant of global health. The presence of contaminated water affects the citizens’ health. Per- and polyfluoroalkyl substances (PFAS) are often referred to as forever chemicals. They pose a persistent and growing threat to drinking water. In the literature, machine learning methods are used to identify the forever chemicals in water. However, traditional methods are not efficient and scalable. Thus, to solve this issue. This study develops a large-scale machine-learning framework for PFAS risk screening in US public water systems. The proposed framework incorporates data ingestion, preprocessing, and feature engineering. We have used SMOTE for correcting imbalanced data. We performed experimentation and also evaluated our ensemble-based framework integrating Gradient boosting, bagging, and meta-learning strategies. The proposed framework achieves a maximum ROC-AUC of 0.9574, with the best-performing stacking ensemble achieving a precision of 0.75, a recall of 0.68, and an F1-score of 0.71. The simulation results show that the proposed ensemble learning framework is useful for screening and identifying water systems.
Rahman et al. (2026) studied this question.