Accurate prediction of spatial distribution of soil heavy metal in complex mining terrain using an improved machine learning method

Abstract

Accurate prediction of heavy metals (HMs) spatial distribution in mining areas is crucial for pollution management. However, predicting the spatial distribution of HMs remains a significant challenge in mining areas with complex terrain and variable contaminant transport pathways. This study aims to optimize the spatial prediction of arsenic (As) distribution in the Shimen realgar mining area, the largest in Asia, by integrating machine learning models with kriging interpolation and feature selection techniques. The results show that the Random Forest (RF) model achieved the best performance in predicting soil As concentration, with an R² of 0.84 for the test data. Incorporating environmental variables improved the spatial prediction accuracy, with RF (R² = 0.76, RMSE = 24.68 mg/kg) and Random Forest Regression Kriging (RFRK) (R² = 0.78, RMSE = 23.46 mg/kg) outperforming ordinary kriging and geographically weighted regression kriging. Importance analysis and recursive feature elimination further optimized the model, leading to a 5 % increase in R² and a reduction of RMSE by 8 %–12.4 %. The optimized RFRK model accurately captured the spatial distribution of As in the mining area, revealing the outward diffusion pattern of As from the smelting plant. The findings highlight the critical role of feature selection in improving prediction accuracy in highly polluted and complex terrain regions, an aspect that has often been overlooked in previous studies. This study provides a practical framework for spatial prediction of contaminants in similar areas, enhancing the understanding of pollution distribution.