Migration modeling of metal(loid)s in soil-groundwater systems from an abandoned mine: Based on multimethod...

Metal(loid)s contamination of mine has been a global environmental challenge. Traditional investigations of metal(loid) distribution patterns and migration behavior in soil-groundwater systems are constrained by the high costs of drilling and sampling limitations, leading to significant uncertainties in contamination assessment. This study presents an integrated approach combining three-dimensional (3D) visualization with Random Forest (RF) modeling and GIS mapping to investigate metal(loid) contamination characteristics and migration behavior in a mining area's soil-groundwater system. We developed an RF model with 1000 decision trees to expand limited drilling data for comprehensive spatial coverage. Model performance was validated using R² and Root Mean Square Error (RMSE) metrics. The validated predictions were integrated into 3D visualization models and analyzed in conjunction with GIS mapping to characterize spatial patterns. Through analysis of temporal groundwater sampling data across wet, dry, and transitional hydrological periods, combined with RF modeling, we visualized metal(loid) distribution patterns and characterized their migration behavior in the soil-groundwater system. This integrated methodology provides a novel framework for investigating metal(loid) distribution and migration in mine soil-groundwater systems, effectively bridging traditional exploration techniques with advanced numerical simulation.