Abstract

Landslides impact on human activities and socio-economic development, especially in mountainous areas. This study focuses on the comparison of the prediction capability of advanced machine learning techniques for the rainfall-induced shallow landslide susceptibility of Deokjeokri catchment and Karisanri catchment in South Korea. The influencing factors for landslides, i.e., topographic, hydrologic, soil, forest, and geologic factors, are prepared from various sources based on availability, and a multicollinearity test is also performed to select relevant causative factors. The landslide inventory maps of both catchments are obtained from historical information, aerial photographs and performed field surveys. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories contain 748 landslide points in training and 219 points in testing areas. Three landslide susceptibility maps using machine learning models, i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN), are prepared and compared. The outcomes of the analyses are validated using the landslide inventory data. A receiver operating characteristic curve (ROC) method is used to verify the results of the models. The results of this study show that the training accuracy of RF is 0.756 and the testing accuracy is 0.703. Similarly, the training accuracy of XGBoost is 0.757 and testing accuracy is 0.74. The prediction of DNN revealed acceptable agreement between the susceptibility map and the existing landslides, with a training accuracy of 0.855 and testing accuracy of 0.802. The results showed that the DNN model achieved lower prediction error and higher accuracy results than other models for shallow landslide modeling in the study area.

Highlights

  • Landslides are one of the major natural hazards worldwide due to unfavorable geological causes, weathering patterns, shallow soil deposits and heavy rainfall

  • The main aim of this research is to evaluate landslide susceptibility based on machine learning techniques, i.e., Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN), in Deokjeokri and Karisanri catchments

  • The results show that the highest value of variance inflation (VIF) is 10.47, and the lowest value of TOL is 0.10, as presented in Table 2, which shows that IR has a multicollinearity problem among the 14 landslide influencing factors (IFs)

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Summary

Introduction

Landslides are one of the major natural hazards worldwide due to unfavorable geological causes, weathering patterns, shallow soil deposits and heavy rainfall. Landslides create dangerous menaces to the environment. The Korean peninsula is currently experiencing climate change effects, including annual temperature, precipitation and the rate of typhoon occurrence [1]. In the Korean Peninsula, one of the main causes of heavy downpour is typhoons. Large-scale typhoons pass over South Korea, leaving a trail of devastation in their path. Shallow-seated landslides often result from heavy downpours accompanying typhoons and torrential rains during the summer season [2]

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