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What is Groundwater Contamination Risk Mapping about?

This study develops an improved DRASTIC vulnerability map for groundwater contamination risk using a multi-model ensemble of machine learning algorithms, including ELM, SVR, M5 Tree, and MARS, applied to multiple aquifer systems in North West Iran. The new approach enhances the original DRASTIC method by reducing subjectivity and achieving a higher correlation coefficient (r = 0.88) in predicting groundwater contamination risk. The findings suggest that the ensemble machine learning models significantly out

Author

Laura Daniela Medina Gonzalez

Language

EN