A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment

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Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree (2019, background): “…et al. 2018), logistic regression (LR) (Colkesen et al. 2016; Aditian et al. 2018), multivariate adaptive regression spline (Zabihi et al. 2016; Chen et al. 2018d), weight of evidence (Tehrany et al. 2014; Guo et al. 2015), index of entropy (Tsangaratos et al. 2017; Chen et al. 2018c),…”
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019, methodology): “ML algorithms that have been used for landslide susceptibility mapping include support vector machine [17,21,47–49], artificial neural network [50,51], decision trees such as naïve Bayes tree (NBT) [5,52], radial basis function (RBF) [53], kernel logistic regression (KLR) [54,55], Bayes’ net (BN) [5”