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

• Published in: Symmetry (Basel) Vol. 11; no. 6; p. 762
• Main Authors: Li, Renwei, Wang, Nianqin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-06-2019
• Subjects: Aerial photography; Altitude; Bivariate analysis; Curvature; evidence belief function; Fault lines; Field investigations; Geographic Information Systems (GIS); Geology; index of entropy; Land use; Land use management; landslide susceptibility; Landslides; Landslides & mudslides; Lithology; Local government; logistic regression; Mapping; Model accuracy; Rivers; Software; Soil erosion; Soil sciences; Statistical analysis; Statistical models; Topography; Vegetation; Vegetation index; weights of evidence; China; Sichuan China
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym11060762

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.