Next Article in Journal
An Eigenvector Centrality for Multiplex Networks with Data
Previous Article in Journal
Battlefield Target Aggregation Behavior Recognition Model Based on Multi-Scale Feature Fusion
 
 
Article

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

by * and *
College of Geology and Environment, Xi’an University of Science and Technology, Xi’an 710054, China
*
Authors to whom correspondence should be addressed.
Symmetry 2019, 11(6), 762; https://doi.org/10.3390/sym11060762
Received: 7 May 2019 / Revised: 26 May 2019 / Accepted: 3 June 2019 / Published: 5 June 2019
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. View Full-Text
Keywords: landslide susceptibility; weights of evidence; evidence belief function; index of entropy; logistic regression; Geographic Information Systems (GIS) landslide susceptibility; weights of evidence; evidence belief function; index of entropy; logistic regression; Geographic Information Systems (GIS)
Show Figures

Figure 1

MDPI and ACS Style

Li, R.; Wang, N. Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression. Symmetry 2019, 11, 762. https://doi.org/10.3390/sym11060762

AMA Style

Li R, Wang N. Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression. Symmetry. 2019; 11(6):762. https://doi.org/10.3390/sym11060762

Chicago/Turabian Style

Li, Renwei, and Nianqin Wang. 2019. "Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression" Symmetry 11, no. 6: 762. https://doi.org/10.3390/sym11060762

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop