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Utilizing machine learning algorithm for finely three-dimensional delineation of soil-groundwater contamination in a typical industrial park, North China: Importance of multisource auxiliary data

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成果类型:
期刊论文
作者:
Liu, Siyan;Yang, Xiao;Shi, Biling;Liu, Zhaoshu;Yan, Xiulan*;...
通讯作者:
Yan, Xiulan;Liang, T
作者机构:
[Liang, Tao; Shi, Biling; Liu, Siyan; Yan, Xiulan; Yang, Xiao; Liu, Zhaoshu] Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China.
[Liang, Tao; Liu, Siyan] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China.
[Zhou, Yaoyu] Hunan Agr Univ, Coll Resources & Environm, Changsha 410128, Peoples R China.
通讯机构:
[Liang, T ; Yan, XL] C
Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China.
Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China.
语种:
英文
关键词:
Back propagation neural network algorithm;Hydrogeological features;Industrial site contamination;Integrated modeling;Three-dimensional spatial analysis
期刊:
Science of The Total Environment
ISSN:
0048-9697
年:
2024
卷:
911
页码:
168598
基金类别:
National Natural Science Foundation of China#&#&#42207456#&#&#U21A2023 Tianjin Municipal Science and Technology Program#&#&#22YFYSHZ00310
机构署名:
本校为其他机构
院系归属:
资源环境学院
摘要:
Intensive industrial activities cause soil contamination with wide variations and even perturb groundwater safety. Precision delineation of soil contamination is the foundation and precondition for soil quality assurance in the practical environmental management process. However, spatial non-stationarity phenomenon of soil contamination and heterogeneous sampling are two key issues that affect the accuracy of contamination delineation model. Taking a typical industrial park in North China as the research object, we constructed a random forest (RF) model for finely characterizing the distributi...

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