Construction of a general-purpose prediction model of sediment collapse location by heavy rainfall using public data and machine learning

Abstract

Sediment disasters caused by heavy rain are increasing in recent years and may cause extensive damage in a wide range like the heavy rain in July 2018. Although many researches are carried out to predict sediment collapse locations, some topographic information used for prediction requires field investigation, etc., and time is needed for prediction. In this study, therefore, we used only publicly available data that can be obtained anywhere in the country without the need for on-site surveys, and constructed a prediction model for sediment collapse locations due to heavy rain using Gradient Boosting Decision Tree, a machine learning method. Furthermore, with the aim of creating a general-purpose prediction model that can be applied to other regions, we selected optimal explanatory variables and considered the interpretability of the prediction model.