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Ensemble learning-based applied research on heavy metals prediction in a soil-rice system

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成果类型:
期刊论文
作者:
Hao, Huijuan;Li, Panpan;Jiao, Wentao;Ge, Dabing;Hu, Chengwei;...
通讯作者:
Jiao, WT;Li, PP
作者机构:
[Jiao, Wentao; Hao, Huijuan] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Beijing 100085, Peoples R China.
[Li, Panpan; Hu, Chengwei; Li, PP] PLA Strateg Support Force Characterist Med Ctr, Informat Ctr, Beijing 100101, Peoples R China.
[Ge, Dabing] Hunan Agr Univ, Coll Resources & Environm, Changsha 410128, Peoples R China.
[Li, Jing] Huludao Cent Hosp, Dept Oncol, Huludao 125001, Peoples R China.
[Lv, Yuntao; Chen, Wanming] Minist Agr & Villages, Risk Assessment Lab Environm Factors Agroprod Qual, Changsha 410005, Peoples R China.
通讯机构:
[Jiao, WT ] C
[Li, PP ] P
Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Beijing 100085, Peoples R China.
PLA Strateg Support Force Characterist Med Ctr, Informat Ctr, Beijing 100101, Peoples R China.
语种:
英文
关键词:
Environmental factor;Gradient boosting machine;Random forest;Sensitivity;Spatial pattern
期刊:
Science of The Total Environment
ISSN:
0048-9697
年:
2023
卷:
898
页码:
165456
基金类别:
National Agricultural Product Quality and Safety Risk Assessment Project, China [GJFP2019036]
机构署名:
本校为其他机构
院系归属:
资源环境学院
摘要:
Accurate prediction of heavy metal accumulation in soil ecosystems is crucial for maintaining healthy soil environments and ensuring high-quality agricultural products, as well as a challenging scientific task. In this study, we constructed a dataset containing 490 sets of multidimensional environmental covariate data and proposed prediction models for heavy metal concentrations (HMC) in a soil-rice system, EL-HMC (including RF-HMC and GBM-HMC), based on Random Forest (RF) and Gradient Boosting Machine (GBM) ensemble learning (EL) techniques. To reasonably evaluate the effectiveness of each mo...

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