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Risk Assessment of Agricultural Soil Heavy Metal Pollution Under the Hybrid Intelligent Evaluation Model

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成果类型:
期刊论文
作者:
Chen, Xinbo;Zhang, Cong;Yan, Ke;Wei, Zhihui;Cheng, Ningshen
通讯作者:
Zhang, C
作者机构:
[Chen, Xinbo; Yan, Ke; Wei, Zhihui] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
[Cheng, Ningshen; Zhang, Cong; Zhang, C] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
通讯机构:
[Zhang, C ] W
Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430023, Peoples R China.
语种:
英文
关键词:
Contamination;Farming;Geographic information systems;Machine learning algorithms;Risk management;farmland protection;geographic information systems;heavy metals;machine learning algorithms;risk assessment;soil
期刊:
IEEE ACCESS
ISSN:
2169-3536
年:
2023
卷:
11
页码:
106847-106858
基金类别:
This work was supported in part by the Major Technical Innovation Projects of Hubei Province under Grant 2018ABA099, in part by the Key Project of the Scientific Research Program of the Hubei Provincial Department of Education under Grant D20201601, and in part by the National Natural Science Foundation of China under Grant 61272278.
机构署名:
本校为第一且通讯机构
院系归属:
电气与电子工程学院
数学与计算机学院
摘要:
As a nationally protected land resource, farmland plays a crucial role in agriculture production and food safety, making the quality of soil and environmental health critically important. Therefore, studying the extent of soil heavy metal pollution in farmland is of great significance for understanding the growth environment of food crops and protecting agricultural land resources. This study addresses the challenge of accurately, quickly, and conveniently assessing the extent of soil heavy metal pollution across an entire research area using a limited number of soil samples. To tackle this is...

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