Estimation on Threshold of Critical Line (CL) for Alerting Landslide using Terrain, Geological, Rainfall and Vegetation Information

Abstract

Critical Line (CL) for alerting landslide warning information in Japan is set based on the past landslide disaster and rainfall information. The accuracy of alerting warning can be gained by re-set the CL considering terrain, geological and vegetation information. This paper presents a way of estimating thresholds of CL (60-minute total rainfall and soil water index (SWI)) from rainfall, terrain, geological and vegetation information by using three types of machine learning algorithms (Random Forest (RF), XGBoost (XGB) and LightGBM (LGB)). Accuracy of the models established by these algorithms were evaluated, and important feature values which gains the accuracy of the model were clarified by evaluating feature importance and Recursive Feature Elimination (RFE). Basis for the estimation of the models were explained by using SHapley Additive exPlanations (SHAP) which can clarify contribution of each information to the results of estimation. Following results were obtained from this study. 1) For estimating thresholds of 60-minute total rainfall, using RF with extracted 10 explanatory variables from 100 variables was the highest coefficient of determination (R²) for testing data among the other models. For estimating thresholds of SWI, using LGB with extracted 10 explanatory variables was the highest R² for testing data among the other models. The extracted variables include geological type number which is distributed by Ministry of Land, Infrastructure, Transport and Tourism in Japan as a geological feature. 2) In the SHAP estimation of 60-minute total rainfall, all models showed a clear tendency for larger values of the maximum 24-hour rainfall, June and August rainfall anomalies to contribute to an increase in the 60-minute total rainfall. For the SWI, the models with the highest R² showed a clear tendency for larger values of 12-hour rainfall and the August rainfall anomalies to contribute to an increase in the SWI.