A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network

Author:

Wang Liang-Jie,Guo Min,Sawada Kazuhide,Lin Jie,Zhang Jinchi

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference38 articles.

1. Ayalew, L. and Yamagishi, H., 2005, The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakudayahiko Mountains, Central Japan. Geomorphology, 65, 15–31.

2. Althuwaynee, O.F., Pradhan, B., Park, H.J., and Lee, J.H., 2014, A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Cantena, 114, 21–36. doi:10.1016/j.catena.2013.10.011

3. Bai, S.B., Wang, J., Guo, N.L., Zhou, P.G., Hou, S.S., and Xu, S.N., 2010, GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorgesarea, China. Geomorphology, 115, 23–31.

4. Bui, D.T., Pradhan, B., Lorfman, O., Revhaug, I., and Dick, O.B, 2012, Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology, 171–172, 12–29. doi:10.1016/j.geomorph.2012.04.023

5. Conforti, M., Pascale, S., Robustelli, G., and Sdao, F., 2014, Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Cantena, 113, 236–250. doi:10.1016/j.catena.2013.08.006

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