Estimation of Distribution of the Weak Soil Layer for Using Geostatistics

Jeong, Jin A; Jang, Won Il

doi:10.5916/jkosme.2011.35.8.1132

Cited by 5 publications

(2 citation statements)

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“…따라서 토석류 발생에 대한 피해를 경감시키기 위하 여 토석류 거동 예측 및 발생 여부 등에 대한 연구가 활발히 진행 중이며, 특히 전국적으로 위험지도를 구축하여 지역별 피해에 대한 예방을 도모하고 있다. Kim et al (2008) (Yoon et al, 2005;Jeong and Jang, 2011;Kim et al, 2012;Oh et al, 2016 (Jenkinson, 1955;Park and Sohn, 2006;Kim et al, 2011).…”

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Method for Establishment of High Reliable Soil Depth Modeling

Min¹,

Yoon²

2018

J. Korean Soc. Hazard Mitig

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The soil depth is an important factor for assessing the risk of debris flow, and thus, it is necessary to obtain reliable value of area. The objective of this study is to suggest the method for deducing highly reliable soil depth modeling in target area through measured data. The seismic survey as a non-destructive method is selected to predict the soil depth based on reference velocity. The reliability is assessed through mathematical function and error ratio, and the range of reliability is determined from 10% to 90%. The outlier data is removed with consideration of the reliability range and then the soil depth modeling is finalized. The geostatistical method is applied to construct the model. The study implies that the suggested method may provide better resolution than immediately applied method of measured values because of eliminating outlier results.

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Section: 서 론unclassified

Method for Establishment of High Reliable Soil Depth Modeling

Min¹,

Yoon²

2018

J. Korean Soc. Hazard Mitig

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“…특히 지리정보시스템(geographic information systems, GIS) 환경에서 지구통계 분석을 이용하여 보간하는 방법들이 주를 이루며, 그중에서도 정규 크리깅(ordinary kriging)을 활용해 추정 (Jeong and Jang, 2011)하거나 보완 (Chung et al, 2001)하는 연구들이 다수 존재하였다. 또한 크리깅을 통해 예측된 값을 검정하기 위해 교차 타당성 검정 기법 중 Leave-one-out을 적용한 연구사례 (Park et al, 2006, Park and Jang, 2008, Park et al, 2012도 사례도 발표되 었다.…”

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

Han

Suh

Choi

2023

Econ. Environ. Geol.

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When creating a soil contamination map using geostatistical techniques, there are various sources that can affect prediction errors. In this study, a grid-based soil contamination map was created from the sampling data of heavy metal concentrations in soil in abandoned mine areas using Ordinary Kriging. Five factors that were judged to affect the prediction error of the soil contamination map were selected, and the variation of the root mean squared error (RMSE) between the predicted value and the actual value was analyzed based on the Leave-one-out technique. Then, using a machine learning algorithm, derived the top three factors affecting the RMSE. As a result, it was analyzed that Variogram Model, Minimum Neighbors, and Anisotropy factors have the largest impact on RMSE in the Standard interpolation. For the variogram models, the Spherical model showed the lowest RMSE, while the Minimum Neighbors had the lowest value at 3 and then increased as the value increased. In the case of Anisotropy, it was found to be more appropriate not to consider anisotropy. In this study, through the combined use of geostatistics and machine learning, it was possible to create a highly reliable soil contamination map at the local scale, and to identify which factors have a significant impact when interpolating a small amount of soil heavy metal data.

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Three-dimensional geologic modeling of the Pohang Basin distributed in Haedo-Dong, Nam-Gu, Pohang-Si

안해성¹

2015

jgsk

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Estimation of Distribution of the Weak Soil Layer for Using Geostatistics

Cited by 5 publications

References 0 publications

Method for Establishment of High Reliable Soil Depth Modeling

Method for Establishment of High Reliable Soil Depth Modeling

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

Three-dimensional geologic modeling of the Pohang Basin distributed in Haedo-Dong, Nam-Gu, Pohang-Si

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