Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)

• Published in: Bulletin of engineering geology and the environment Vol. 78; no. 1; pp. 247 - 266
• Main Authors: Chen, Wei, Yan, Xusheng, Zhao, Zhou, Hong, Haoyuan, Bui, Dieu Tien, Pradhan, Biswajeet
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 04.02.2019; Springer Nature B.V
• Subjects: Aerial photographs; Aerial photography; Aerial surveys; Area; Basis functions; Bayesian analysis; Conditioning; Curvature; Data mining; Data processing; Distance; Earth and Environmental Science; Earth Sciences; Foundations; Geoecology/Natural Processes; Geoengineering; Geological engineering; Geotechnical Engineering & Applied Earth Sciences; Hydraulics; Land use; Landslides; Landslides & mudslides; Learning algorithms; Lithology; Machine learning; Mathematical models; Modelling; Nature Conservation; Original Paper; Predictions; Radial basis function; Rain; Rainfall; Ratios; Regression analysis; Rivers; Statistical analysis; Statistical tests; Surveys; Tolerances; Tolerances (dimensional); Training; Variance analysis; Vegetation index; China; RBF network; Landslide; Kernel logistic regression; Naive Bayes; China

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landsl...