Research on machine learning-based prediction of available Cu and Zn and key factor identification during the aging process
To explore the aging process of copper(Cu)and zinc(Zn)in various soil types and their key influencing factors,this study conducted a 90-day incubation experiment with exogenous additions of Cu and Zn to 12 different soil types.Predictive models for available Cu and Zn were developed using kinetic models,stepwise linear regression,and machine learning approaches.The SHAP(Shapley Additive Explanations)method was employed to analyze the impact of key soil factors on the bioavailability of Cu and Zn.The results indicated that available Cu and Zn rapidly declined within the first 30 days,followed by a slower decrease,with pH having a significant effect on the aging rate,particularly in alkaline soils.Kinetic models revealed that the aging process of Cu was primarily controlled by micropore diffusion,while the aging process of Zn was more complex and not entirely dependent on diffusion.Stepwise linear regression analysis indicated that soil conductivity and particle size distribution significantly influenced the bioavailability of Cu and Zn.In addition,a comparison of four machine learning models[random forest,support vector regression,eXtreme gradient boosting(XGBoost),and symbolic regression]demonstrated that the XGBoost model had the highest predictive accuracy.SHAP analysis further identified that iron oxides and organic matter content were the most critical factors affecting available Cu and Zn.The effect of pH on available Cu and Zn varied significantly,with a strong interaction between iron oxides and pH in the prediction of available Cu.Overall,this study combined kinetic models,stepwise linear regression,and machine learning methods to reveal the major driving factors and their interactions in the aging process of Cu and Zn in soils.
copperzincbioavailability predictionextreme gradient boosting(XGBoost)kinetic equationaging process
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