PREDICTION OF LOCATIONS OF LANDSLIDE DUE TO HEAVY RAINFALL BASED ON MACHINE LEARNING TECHNIQUES AND EXPLAINABLE ARTIFICIAL INTELLIGENCE

Abstract

The July 2018 heavy rainfall in the Western Japan caused extensive disasters over a wide area. From a long-term perspective, the occurrence of such heavy rainfall is on the increase, and it is important to predict where landslides will occur. In this study, we developed numerical models for predicting the locations of landslides using random forests, a machine learning technique. Two models with different explanatory variables were developed, and their prediction accuracies were compared. In addition, we also used SHAP, a type of explainable artificial intelligence (XAI) that has been studied in recent years, to provide global and local explanations of the numerical models.