ASSESSMENT OF LANDSLIDE HAZARDS USING LOGISTIC REGRESSION WITH HIGH-RESOLUTION RADAR RAINFALL OBSERVATION AND GEOLOGICAL FACTOR

Abstract

 We attempt to investigate the efficiency of applying a lithology factor with high-resolution XRAIN observation to accurate landslide hazard estimation. This study presents a model of landslide mapping using logistic regression with geological and high-resolution hydrometeorological factors, and analyzes hazardous conditions of landslide disasters occurred in Kure, Hiroshima during the heavy rainfall event in July of 2018. Being identical to the practical method of landslide early warning, the hydrometeorological factors are hourly cumulative rainfall and soil-water index. The lithology factor is derived from the seamless geological map. As a first trial, the model was simply calibrated using linear logistic regression on a recent landslide inventory composing of 646 events in Chugoku Region after 2012. 85% and 15% of events are used for training and accuracy test, and the calibrated model achieves a high accuracy of 91.8%. To verify, our model was applied to estimate landslide occurrence during the heavy rainfall in Kure, Hiroshima. The result verified our model can estimate highly accurate occurrence location.