Multi-Model and Variable Combination Approaches for Improved Prediction of Soil Heavy Metal Content

Soil heavy metal contamination poses significant risks to ecosystems and human health, necessitating accurate prediction methods for effective monitoring and remediation. We propose a multi-model and variable combination framework to improve the prediction of soil heavy metal content by integrating diverse environmental and spatial features. The methodology incorporates environmental variables (e.g., soil properties, remote sensing indices), spatial autocorrelation measures based on nearest-neighbor distances, and spatial regionalization variables derived from interpolation techniques such as ordinary kriging, inverse distance weighting, and trend surface analysis. These variables are systematically combined into six distinct sets to evaluate their predictive performance. Three advanced models—Partial Least Squares Regression, Random Forest, and a Deep Forest variant (DF21)—are employed to assess the robustness of the approach across different variable combinations. Experimental results demonstrate that the inclusion of spatial autocorrelation and regionalization variables consistently enhances prediction accuracy compared to using environmental variables alone. Furthermore, the proposed framework exhibits strong generalizability, as validated through subset analyses with reduced training data. The study highlights the importance of integrating spatial dependencies and multi-source data for reliable heavy metal prediction, offering practical insights for environmental management and policy-making. Compared to using environmental variables alone, the full framework incorporating spatial features achieved relative improvements of 18–23% in prediction accuracy (R²) across all models, with the Deep Forest variant (DF21) showing the most substantial enhancement. The findings advance the field by providing a flexible and scalable methodology adaptable to diverse geographical contexts and data availability scenarios.