Integrating pollution indices, spatial interpolation, and machine learning for soil contamination analysis along the Zarqa River, Jordan

The integration of pollution indices, GIS, and machine learning models provided a high-resolution assessment of contamination hotspots, and XGBoost demonstrated the highest predictive accuracy for Fe and the decision tree and geographically weighted regression models also demonstrated robust performance for Zn, Mn, Cr, and Co.