农业环境科学学报2024，Vol.43Issue(11)：2534-2544,11.DOI:10.11654/jaes.2024-0843
基于机器学习对铜和锌在土壤中的老化预测和关键因子识别
Research on machine learning-based prediction of available Cu and Zn and key factor identification during the aging process
摘要
Abstract
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.关键词
铜/锌/生物有效性预测/极限梯度提升(XGBoost)/动力学过程/老化Key words
copper/zinc/bioavailability prediction/extreme gradient boosting(XGBoost)/kinetic equation/aging process分类
资源环境引用本文复制引用
夏菲洋,和长城,陆晓松,王玉军,杨敏,范婷婷..基于机器学习对铜和锌在土壤中的老化预测和关键因子识别[J].农业环境科学学报,2024,43(11):2534-2544,11.基金项目
国家重点研发计划项目(2021YFC1809102)National Key Research and Development Program of China(2021YFC1809102) （2021YFC1809102）
