Prediction of volume of shallow landslides due to rainfall using data-driven models

Abstract. Landslides due to rainfall are among most destructive natural disasters that cause property damages, huge financial losses and human deaths in different parts of the World. To plan for mitigation and resilience, the prediction of the volume of rainfall-induced landslides is essential to understand the relationship between the volume of soil materials debris and their associated predictors. Objectives of this research are to construct a model by utilizing advanced data-driven algorithms (i.e., ordinary least square or Linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), and deep neural network (DNN), K-nearest neighbor (KNN) and Ridge regression (RR)) for the prediction of the volume of landslides due to rainfall considering geological, geomorphological, and environmental conditions. Models were tested on the Korean landslide dataset to observe the best-performing model, and among tested algorithms, the extreme gradient boosting ranked high with the coefficient of determination (R² = 0.85) and mean absolute error (MAE = 150.421 m³). The volume of landslides was strongly influenced by slope length, drainage status, slope angle, aspect, and age of trees. The anticipated volume of landslide can be important for land use allocation and efficient landslide risk management.