Abstract
The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.
Highlights
Landslides often occur in mountainous and hilly areas and are one of the most dangerous geological disasters [1]
The training dataset was used to evaluate explanatory variables and the Pearson correlation coefficient between pairs of explanatory variables was calculated (Table 2). It can be seen from the results that the lowest PCC value is −0.009, which happened between altitude and normalized difference vegetation index (NDVI), and the highest PCC value happened between slope aspect and distance to rivers (0.368)
Altitude, slope aspect, mean annual precipitation, slope angle, lithology, distance variables, namely, altitude, slope aspect, mean annual precipitation, slope angle, lithology, distance to roads, land use, distance and NDVI, NDVI,were wereselected selectedand and potential to roads, land use, distancetotorivers, rivers,distance distance to to faults, faults, and thethe potential multicollinearity problem among them waswas detected by PCC, variance inflation factor (VIF), VIF, and TOL
Summary
Landslides often occur in mountainous and hilly areas and are one of the most dangerous geological disasters [1]. Landslides can cause huge economic losses and a large number of casualties. Almost 1000 people and 4 billion dollars are lost annually in the world [2], and this figure still keeps growing. China is a region where landslides frequently occur; it has been reported that 7122 geological disasters occurred in 2017, causing 327 deaths, 173 injured, 25 missing, and a loss of 3.54 billion CNY [3]. In northwestern China, landslides pose a greater threat to Entropy 2018, 20, 884; doi:10.3390/e20110884 www.mdpi.com/journal/entropy. Enormous manpower and material resources may be required to control and renovate every landslide. Predicting landslide occurrence is both valuable and important
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
