Landslide susceptibility assessment in the Nantian area of China: a comparison of frequency ratio model and support vector machine

It is significant to do landslide susceptibility assessment (LSA) accurately and efficiently using an appropriate model for landslide prediction and prevention. This article aims to compare the frequency ratio (FR) model with the support vector machine (SVM), for mapping the landslide susceptibility of Nantian area in southeastern hilly area, China. To begin, 70 recorded landslides are identified through field investigation and the land and recourse department, 50% of the landslide grid cells are used to train the models and the remaining 50% of the landslide grid cells are used to test the models. Ten environmental factors are used in the modeling of LSA, including the elevation, slope, aspect, plan curvature, profile curvature, relief amplitude, lithology factor, distance to river, Normalized Difference Build-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Then the landslide susceptibility maps of Nantian area are produced by the FR and SVM models, respectively. Finally, the accuracies and efficiencies of both two models are evaluated and compared. The results show that the landslide susceptibility distribution characteristics of Nantian area are explored well by the two models, and the FR model has higher prediction rate and is considerably more efficient than SVM for LSA.