![rf online maps rf online maps](https://i.stack.imgur.com/dkhvU.jpg)
The results showed that the susceptibility maps produced from the different models were all reasonable. Next, the landslide density (LD), frequency ratio (FR), the area under the curve (AUC) and other indicators were used to validate the rationality, performance and accuracy of the models. Then, landslide susceptibility mapping was carried out using the five models, respectively. The 23,169 slope units were generated from a Digital Elevation Model and the corresponding 10 conditioning factor layers were produced from both geological and geographical data. Firstly, 10 landslide conditioning factors were selected, namely slope-angle, altitude, slope aspect, degree of relief, lithology, distance to rivers, distance to faults, distance to roads, average annual rainfall and normalized difference vegetation index. A traditional statistical certainty factor model (CF), a machine learning support vector machine model (SVM) and random forest model (RF), along with a hybrid CF-SVM model and a CF-RF model were applied to analyze landslide susceptibility.
![rf online maps rf online maps](https://www.mdpi.com/applsci/applsci-08-01369/article_deploy/html/images/applsci-08-01369-g005.png)
Slope units were selected as the basic mapping units. Toward this end, this paper presents a case study in Ningqiang County, Shanxi Province, China. Landslide susceptibility mapping is very important for landslide risk evaluation and land use planning. 2National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, China.1College of Geology and Environment, Xi'an University of Science and Technology, Xi'an, China.Zhou Zhao 1*, Zeng yuan Liu 1 and Chong Xu 2