Risk Identification of Heavy Rain-induced Muck soil Landslide

Abstract

Based on Monte Carlo method, this paper calculates landslide probability of muck soil slopes under different rainfall time, rainfall intensity, soil permeability coefficient and slope angle, thus obtaining the probability samples of muck soil landslides. On this basis, logistic regression method of nonlinear classification is used for data fitting and analysis, thus establishing nonlinear function . Function expression is derived by data fitting, and a landslide probability evaluation model is constructed. Based on analysis of engineering examples, the error between this method and the numerical calculation results is within 10%, and the evaluation results are reasonable. It provides theoretical support for rapid identification of muck soil landslide risk under heavy rain conditions.