Machine learning combined with geodetector to predict the spatial distribution of soil heavy metals in mining areas.

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An accurate understanding of the spatial distribution of soil heavy metals (HMs) is crucial for the effective prevention of soil pollution and remediation strategies. Traditional machine learning models often overlook the spatially stratified heterogeneity inherent to environmental data, which can impair predictive accuracy. Therefore, we combined the Geodetector model (GDM) with machine learning models. The factor detection results were used to screen covariates to consider the local spatial heterogeneity of model features. The interaction detection results were used to construct spatially stratified covariates to consider the spatially stratified heterogeneity of model features. The results showed that covariate screening largely avoided the introduction of redundant features. The constructed spatially stratified covariates improved the predictive performance of the model (both the R-squared (R2) and root mean square error (RMSE) of different models were optimized). Among these, the XGB model exhibited the best performance. Analysis of the factors influencing Pb and Cr revealed that the interaction between pH and NDVI was the main determinant of Pb spatial distribution (q=0.3516, XGB Importance Score=93). In contrast, the interaction between DEM and pH (q=0.7156, XGB Importance Score=121) as well as the distance to waste piles (q=0.6390, XGB Importance Score=66), were the main driving factors for the spatial distribution of Cr. The current work provides an improved approach for interrogating the factors that influence HM distribution in soil. This study offers valuable insights into the spatial distribution of soil HMs. The proposed methodology can be applied in future soil pollution assessments and environmental management strategies, thus contributing to more precise pollution prevention and remediation efforts.