Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms

Version 1 : Received: 2 August 2020 / Approved: 4 August 2020 / Online: 4 August 2020 (11:13:02 CEST)

How to cite: Pradhan, A.M.S.; Kim, Y. Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms. Preprints 2020, 2020080089 (doi: 10.20944/preprints202008.0089.v1). Pradhan, A.M.S.; Kim, Y. Landslide Susceptibility Mapping at Two Adjacent Catchments Using Advanced Machine Learning Algorithms. Preprints 2020, 2020080089 (doi: 10.20944/preprints202008.0089.v1).

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 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 performing field survey. In this study, Deokjeokri catchment is considered as a training area and Karisanri catchment as a testing area. The landslide inventories content 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 0.757 and the testing accuracy is 0.74. Similarly, training accuracy of XGBoost is 0.756 and testing accuracy is 0.703. The prediction of DNN revealed acceptable agreement between susceptibility map and the existing landslides with training and testing accuracy of 0.855 and 0.802, respectively. 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

Subject Areas

Deep Neural Network; Extreme Gradient Boosting; Random Forest; Landslide Susceptibility

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