Machine Learning-Based Analysis of Heavy Metal Migration Under Acid Rain: Insights from the RF and SVM Algorithms

Acid rain alters soil chemistry significantly and is a key driver of heavy metal pollution. This study investigates the environmental impact of acid rain-induced heavy metal migration in the Siding Lead–Zinc mining area in south China. Tailings, surrounding soils, and riverbed sediments were examined through simulated acid rain soil column leaching experiments. Leachate parameters—including pH, redox potential (Eh), total dissolved solids (TDSs) and heavy metal concentrations—were used to develop machine learning models (Random Forest and Support Vector Machine) to quantify the influence of environmental factors on metal migration. The results showed that leachates were generally alkaline and reductive after leaching, with Cd, Pb, and Zn as the dominant migrating metals. Leachates from tailings and nearby soils exceeded safe drinking water standards, with significantly higher cumulative metal release than other samples. The RF model outperformed the SVM model in predicting heavy metal concentrations. Feature importance analysis revealed that, beyond sample characteristics, pH and Eh were critical factors driving metal migration. Zn and Cd showed strong sensitivity to these parameters, with pH and Eh contributing over 80% to their migration. The findings highlight that acid rain can enhance the solubility and migration of heavy metals, posing a serious threat to the quality of surrounding water and underscoring the requirement for effective mitigation strategies to protect the ecological environment in mining areas.