Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution

This study develops a predictive framework to model the Heavy Metal Pollution Index (HPI) in groundwater, integrating response transformations with nested cross-validated ensemble machine learning. It aims to overcome challenges posed by statistical complexity and spatial heterogeneity of contaminants that affect conventional prediction methods.