Advanced three-dimensional prediction model based on stable machine learning for soil pollution: A case study from a...

With over five million contaminated sites worldwide, accurately characterizing the three-dimensional (3D) distribution of soil contamination is critical for effective risk assessment and site remediation. However, current 3D interpolation methodologies often fail to simultaneously account for spatial correlation and spatial heterogeneity, both of which are critical for capturing the complex spatial structure of subsurface contamination. This study developed a refined 3D interpolation model that integrates site characteristics, spatial position, spatial correlation, and spatial heterogeneity to simulate site contamination and quantify prediction uncertainty. The proposed machine learning (ML) model achieved high predictive performance, with coefficient of determination (R²) values above 0.73 for four heavy metals (HMs). To enhance model generalizability, a stability analysis framework was developed alongside a novel model selection strategy based on random dataset partitioning and random ordering of input covariates, and 1000 random simulations could provide a reliable basis for model screening. This study introduces a new, precise 3D spatial interpolation method. Owing to the easy accessibility of its covariates, it offers high versatility, making a significant contribution to site assessment and remediation efforts.