Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm

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A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment (2018, background): “Slope aspect is a pivotal conditioning factor that is widely used in LSM (Kavzoglu et al. 2015; Meten et al. 2015; Chen et al. 2016a).”
A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping (2018, methodology): “…subspace fuzzy rules), decision trees (e.g. regression trees and CART), tree-based hybrid algorithms (e.g. functional trees and logistic model trees) (Gomez and Kavzoglu 2005; Kavzoglu, Sahin, and Colkesen 2014, 2015a; Tien Bui et al. 2016a,b; Colkesen, Sahin, and Kavzoglu 2016; Pham et al. 2016).”
Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods (2017, background): “…and Altural 2013) Over the decades, the topic of landslide susceptibility mapping has been turning into one of the most attentions to many researchers throughout the world (Dou et al. 2015c; Hong et al. 2015b; Kavzoglu et al. 2015; Pareek et al. 2013; Shahabi et al. 2014; Tien Bui et al. 2014b).”