An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district

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A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping (2018, methodology): “…the confidence for each class, wj , by the average combination method: lðxÞ ¼ 1L PL i¼1 di;jðxRai Þ, j ¼ 1; . . . ; c Assign xto the class with the largest confidence. method to validate the performance of a new algorithm in landslide related studies (Kavzoglu et al. 2014; Eker et al. 2015).”
Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. (2018, unknown): “The receiver operating characteristics curve The receiver operating characteristic (ROC) curve defines the performance of a binary classifier system as its discrimination threshold changes (Kavzoglu et al., 2015;Wang et al., 2015).”
Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China) (2016, methodology): “In this method, the weighting value for each categorical unit is defined as the natural logarithm of the landslide density in the categorical unit divided by the landslide density in the whole studied area (Kavzoglu et al. 2015; Regmi et al. 2014).”