A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility…

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An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, methodology): “Althuwaynee et al. (2014) investigated the performance of Dempster–Shafer-based evidential belief function and analytical hierarchy process, and modeling performances were compared with the traditional LR method.”
Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018, background): “TRI shows the concave and convex upward slopes (Althuwaynee et al. 2014), and was calculated as follows: where max denotes the pixel largest value in nine rectangular altitude neighbourhoods, and min denotes the minimum value (Riley 1999).”
A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network (2016, background): “…Shakoor, 2010; Xu et al., 2012), weights of evidence (Regmi et al., 2010a, 2010b), artificial neural networks (Poudyal et al., 2010; Pradhan et al., 2010; Yilmaz, 2010; Bui et al., 2012; Conforti et al., 2014), and decision trees (Saito et al., 2009; Yeon et al., 2010; Althuwaynee et al., 2014).”