Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Renwei Li,Nianqin Wang

doi:10.3390/sym11060762

Abstract

The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.

Highlights

As one of the most frequently-occurring geological disasters in the world, landslides have triggered a series of threats to human society including casualties, economic losses, infrastructure destruction, and geological environment problems [1,2,3]
There are two commonly used statistical parameters in multicollinearity analysis, namely tolerance (TOL) and variance inflation factor (VIF), and they are a pair of reciprocals
The results indicate all the factors are independent from each other

Summary

Introduction

As one of the most frequently-occurring geological disasters in the world, landslides have triggered a series of threats to human society including casualties, economic losses, infrastructure destruction, and geological environment problems [1,2,3]. To reduce the losses, it is absolutely necessary to study the landslide susceptibility in a region [4,5]. According to the previous researches, landslide susceptibility can be roughly defined as the landslide occurrence probability in an area under the synergistic effect of a number of regional geological environmental factors [6,7]. Due to the large number and variability of landslide conditioning factors involved in the process, it is difficult to predict landslide-prone areas. The methods used in previous studies can be roughly divided into two types: qualitative and quantitative, for example, analytic hierarchy process (AHP) is the most commonly-used qualitative approach in landslide susceptibility mapping [14,15,16].

Objectives

Methods

Results

Conclusion