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@article{ART002867334},
author={Hyewon Kang and PARKSANGJIN and Lee, Dong Kun},
title={Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms},
journal={Journal of Environmental Impact Assessment},
issn={1225-7184},
year={2022},
volume={31},
number={4},
pages={214-225}

TY - JOUR
AU - Hyewon Kang
AU - PARKSANGJIN
AU - Lee, Dong Kun
TI - Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms
JO - Journal of Environmental Impact Assessment
PY - 2022
VL - 31
IS - 4
PB - Korean Society Of Environmental Impact Assessment
SP - 214
EP - 225
SN - 1225-7184
AB - This study emphasized the soil of environmental impact assessment to devise measures to minimize the negative impact of project implementation on the environment. As a series of efforts for impact assessment procedures, a national inventory-based database was established for urban development projects, and three machine learning model performance evaluation as well as soil pollutant concentration distribution mapping were conducted. Here, nine soil pollutants were mapped to the metropolitan area of South Korea using the Random Forest model, which showed the best performance. The results of this study found that concentrations of Zn, F, and Cd were relatively concerned in Seoul, where urbanization is the most active. In addition, in the case of Hg and Cr6+, concentrations were detected below the standard, which was derived from a lack of pollutants such as industrial and industrial complexes that affect contents of heavy metals. A significant correlation between land cover and pollutants was inferred through the spatial distribution mapping of soil pollutants. Through this, it is expected that efficient soil management measures for minimizing soil pollution and planning decisions regarding the location of the project site can be established.
KW - urban development;heavy metals in soil;spatial statistics;environmental impact assessment;machine learning
DO -
UR -
ER -

Hyewon Kang, PARKSANGJIN and Lee, Dong Kun. (2022). Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms. Journal of Environmental Impact Assessment, 31(4), 214-225.

Hyewon Kang, PARKSANGJIN and Lee, Dong Kun. 2022, "Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms", Journal of Environmental Impact Assessment, vol.31, no.4 pp.214-225.

Hyewon Kang, PARKSANGJIN, Lee, Dong Kun "Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms" Journal of Environmental Impact Assessment 31.4 pp.214-225 (2022) : 214.

Hyewon Kang, PARKSANGJIN, Lee, Dong Kun. Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms. 2022; 31(4), 214-225.

Hyewon Kang, PARKSANGJIN and Lee, Dong Kun. "Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms" Journal of Environmental Impact Assessment 31, no.4 (2022) : 214-225.

Hyewon Kang; PARKSANGJIN; Lee, Dong Kun. Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms. Journal of Environmental Impact Assessment, 31(4), 214-225.

Hyewon Kang; PARKSANGJIN; Lee, Dong Kun. Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms. Journal of Environmental Impact Assessment. 2022; 31(4) 214-225.

Hyewon Kang, PARKSANGJIN, Lee, Dong Kun. Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms. 2022; 31(4), 214-225.

Hyewon Kang, PARKSANGJIN and Lee, Dong Kun. "Predicting Concentrations of Soil Pollutants and Mapping Using Machine Learning Algorithms" Journal of Environmental Impact Assessment 31, no.4 (2022) : 214-225.