Journal Article

Strategy of oversampling geotechnical parameters through geostatistical, SMOTE, and CTGAN methods for assessing susceptibility of landslide

Min, Dae-Hong, Yoon, Hyung-Koo, Kim, Sewon, Kim, YoungSeok

2024

Overview

The target slope is generally divided into grids to predict landslide susceptibility; however, it is difficult to acquire all geotechnical properties for each grid. The objective of this study is to examine oversampling characterization for each grid using geostatistical method and oversampling algorithms. Kriging, which is widely used in geotechnical engineering, is selected as a geostatistical method, and the synthetic minority oversampling technique (SMOTE) and conditional tabular generative adversarial network (CTGAN) are applied to perform oversampling as deep learning algorithms. The target area is divided into 900, 1800, 3600, 9000, 18,000, and 180,000 grids to determine the oversampling behavior for each grid size. The soil cohesion, slope angle, soil density, soil depth, and friction angle, which are input parameters in an infinite slope stability model, are measured through laboratory and field tests, and then the oversampling is performed. The distributions of oversampled data are analyzed with a comparison of mean and standard deviation, and the SMOTE showed a similar distribution with measured values at both 1800 and 3600 grids. Outlier analysis is also performed to suggest a reasonable confidence level for each input parameter, and the resolution of each geotechnical parameter is increased at the 5% confidence level. Finally, the mean absolute error (MAE) is reduced to around 62–69% and 41–43% for arithmetical mean and standard deviation. This study shows that not only kriging but also deep learning algorithms can be used when oversampling is required in the fields of geotechnical and geological engineering.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Confidence intervals / Data analysis / Deep learning / Error reduction / Field tests / Generative adversarial networks / Geostatistics / Geotechnical engineering / Landslides / Learning algorithms / Machine learning / Outlier analysis / Outliers (landforms) / Outliers (statistics) / Oversampling / Parameters / Slope stability / Soil density / Soil depth / Soil properties / Soils / Standard deviation