GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China

By Wei Chen, Wenping Li, Huichan Chai, En-ke Hou, Xiaoqin Li, Xiao Ding. Environmental Earth Sciences, 2015

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A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility (2017, methodology): “Globally, several statistical models combined with GIS have been used for landslide susceptibility assessment, such as statistical index (Chen et al., 2016a; Constantin et al., 2011; Nasiri Aghdam et al., 2016), index of entropy (IOE) (Constantin et al., 2011; Devkota et al., 2013; Youssef et al., 2”
A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping (2017, methodology): “…value, as presented in Table 5, is simply the density of landslides among the classes and is calculated by dividing the susceptibility class area percent values by the landslide seed cell percent values (Chen et al., 2016; Conforti, Robustelli, Muto, & Critelli, 2012; Yilmaz, Topal, & Süzen, 2012).”