Optimization of Soil Contamination Distribution Prediction Error using Geostatistical Technique and Interpretation of Contributory Factor Based on Machine Learning Algorithm-Reference-Cited by-同舟云学术

Optimization of Soil Contamination Distribution Prediction Error using Geostatistical Technique and Interpretation of Contributory Factor Based on Machine Learning Algorithm

Published:2023-06-30 Issue:3 Volume:56 Page:331-341
ISSN:1225-7281
Container-title:Economic and Environmental Geology
language:en
Short-container-title:Econ. Environ. Geol.

Author:

Han Hosang¹,Suh Jangwon²,Choi Yosoon³

Affiliation:

1. Energy and Mineral Resources Engineering, Kangwon National University, Samcheok 25913, Republic of Korea

2. Energy Resources and Chemical Engineering, Kangwon National University, Samcheok 25913, Republic of Korea

3. Energy Resources Engineering, Pukyong National University, Busan 48513, Republic of Korea

Publisher

The Korean Society of Economic and Environmental Geology

Subject

Economic Geology,Geology,Environmental Science (miscellaneous)

Link

http://pdf.medrang.co.kr/kseeg/2023/056/kseeg056-03-331.pdf

Reference27 articles.

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3. Chung, S., Kang, D., Park, H. and Shim, B. (2000) Application of Geostatistical Methods for the Analysis of Groundwater Contamination in Pusan. The Journal of Engineering Geology, v.10(3), p.247-261.

4. Chung, S., Shim, B., Kang, D., Kim, B., Park, H., Won, J. and Kim, G. (2001) Interpolation of Missing Groundwater Level Data Using Kriging at a National Groundwater Monitoring Well. Journal of the Geological Society of Korea, v.37(3), p.421-430.

5. Smooth interpolation of scattered data by local thin plate splines