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<p> This study investigates how machine learning integrated with interpretable artificial intelligence can optimize ecological buffer zone design for mining remediation. We hypothesize that SHAP analysis can identify key environmental factors controlling heavy metal behavior and enable data-driven optimization of buffer zone systems. Environmental data from 440 sampling points across a 3.2 km² mining-impacted watershed were analyzed using six machine learning algorithms integrated with SHAP analysis. Spatial modeling characterized metal transport patterns, and multi-objective optimization designed a three-tier buffer zone system. The machine learning framework achieved high predictive accuracy (R² = 0.90–0.96) for six heavy metals. SHAP analysis identified soil organic matter (mean absolute SHAP = 0.55) and pH (0.53) as primary controlling factors. Spatial modeling revealed metal-specific transport patterns with half-distances ranging from 462 m (Zn) to 1,155 m (Cr). The optimized buffer system achieved 62.5% metal flux reduction with 11.3:1 benefit-cost ratio. Integration of machine learning with SHAP analysis provides an interpretable framework for ecological buffer zone optimization. The identified relationships between environmental factors and metal behavior, while model-driven rather than mechanistic, are consistent with geochemical principles and enable targeted remediation strategies. This approach demonstrates the potential for intelligent, economically viable mining remediation that can be adapted to similar contaminated watersheds worldwide.</p>

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Machine Learning Enhanced Multi Level Ecological Buffers Revolutionize Mining Cleanup: Game-Changing Solution?

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Peng Chen,
  • Feiyun Huang,
  • Rong Zhang

摘要

This study investigates how machine learning integrated with interpretable artificial intelligence can optimize ecological buffer zone design for mining remediation. We hypothesize that SHAP analysis can identify key environmental factors controlling heavy metal behavior and enable data-driven optimization of buffer zone systems. Environmental data from 440 sampling points across a 3.2 km² mining-impacted watershed were analyzed using six machine learning algorithms integrated with SHAP analysis. Spatial modeling characterized metal transport patterns, and multi-objective optimization designed a three-tier buffer zone system. The machine learning framework achieved high predictive accuracy (R² = 0.90–0.96) for six heavy metals. SHAP analysis identified soil organic matter (mean absolute SHAP = 0.55) and pH (0.53) as primary controlling factors. Spatial modeling revealed metal-specific transport patterns with half-distances ranging from 462 m (Zn) to 1,155 m (Cr). The optimized buffer system achieved 62.5% metal flux reduction with 11.3:1 benefit-cost ratio. Integration of machine learning with SHAP analysis provides an interpretable framework for ecological buffer zone optimization. The identified relationships between environmental factors and metal behavior, while model-driven rather than mechanistic, are consistent with geochemical principles and enable targeted remediation strategies. This approach demonstrates the potential for intelligent, economically viable mining remediation that can be adapted to similar contaminated watersheds worldwide.