中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 地理科学与资源研究所
  3. 中国科学院地理科学与资源研究所
  4. 自然资源与环境安全研究部
  5. 外文论文
Spatial prediction of soil contamination based on machine learning: a review

文献类型：期刊论文

作者Zhang, Yang2; Lei, Mei2; Li, Kai; Ju, Tienan
刊名FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING
出版日期2023-08-01
卷号17期号:8
ISSN号2095-221X
关键词Soil contamination Machine learning Prediction Spatial distribution
DOI10.1007/s11783-023-1693-1
文献子类Review
英文摘要Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.
WOS关键词ARTIFICIAL NEURAL-NETWORKS ; HEAVY-METALS POLLUTION ; BACKGROUND CONCENTRATIONS ; HEALTH-RISK ; MODELS ; ACCURACY ; SCIENCES
WOS研究方向Engineering ; Environmental Sciences & Ecology
出版者HIGHER EDUCATION PRESS
WOS记录号WOS:000935927700002
源URL[http://ir.igsnrr.ac.cn/handle/311030/190230]  
专题资源利用与环境修复重点实验室_外文论文
作者单位1.Univ Chinese Acad Sci, Sino Danish Coll, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yang,Lei, Mei,Li, Kai,et al. Spatial prediction of soil contamination based on machine learning: a review[J]. FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING,2023,17(8).
APA Zhang, Yang,Lei, Mei,Li, Kai,&Ju, Tienan.(2023).Spatial prediction of soil contamination based on machine learning: a review.FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING,17(8).
MLA Zhang, Yang,et al."Spatial prediction of soil contamination based on machine learning: a review".FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING 17.8(2023).

入库方式： OAI收割

来源：地理科学与资源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。