Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models

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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, background): “…al., 2015; Pradhan, 2013), multivariate adaptive regression splines (MARS) (Conoscenti et al., 2015; Felicísimo et al., 2013; Vorpahl et al., 2012; Wang et al., 2015a), neural networks (Lee et al., 2007; Park et al., 2013; Tien Bui et al., 2016c; Yilmaz, 2010), fuzzy k-nearest neighbor (Tien Bui…”
A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping (2018, background): “…landslide susceptibility mapping (LSM) has been the most concentrated topic in landslide related studies (Carrara et al. 1991; Guzzetti et al. 2005; Wang et al. 2015; Kutlug Sahin, Ipbuker, and Kavzoglu CONTACT Emrehan Kutlug Sahin e.sahin@gtu.edu.tr 2018 Informa UK Limited, trading as Taylor &…”
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).”