Articles | Volume 17, issue 8
https://doi.org/10.5194/nhess-17-1411-2017
https://doi.org/10.5194/nhess-17-1411-2017
Research article
 | 
25 Aug 2017
Research article |  | 25 Aug 2017

Landslide susceptibility mapping on a global scale using the method of logistic regression

Le Lin, Qigen Lin, and Ying Wang

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Cited articles

Alimohammadlou, Y., Najafi, A., and Gokceoglu, C.: Estimation of rainfall-induced landslides using ANN and fuzzy clustering methods: A case study in Saeen Slope, Azerbaijan province, Iran, Catena, 120, 149–162, https://doi.org/10.1016/j.catena.2014.04.009, 2014.
Allison, P. D.: Logistic regression using the SAS system: theory and application, Wiley Interscience, New York, 2001.
Anbalagan, R.: Landslide hazard evaluation and zonation mapping in mountainous terrain, Eng. Geol., 32, 269–277, https://doi.org/10.1016/0013-7952(92)90053-2, 1992.
Atkinson, P. M. and Massari, R.: Generalised linear modelling of susceptibility to landsliding in the central Apennines, Italy, Comput. Geosci., 24, 373–385, https://doi.org/10.1016/s0098-3004(97)00117-9, 1998.
Ayalew, L. and Yamagishi, H.: The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan, Geomorphology, 65, 15–31, https://doi.org/10.1016/j.geomorph.2004.06.010, 2005.
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Short summary
To address the issue of what can influence the occurrence of landslides on a global scale and to what impact those factors can have in a relatively objective way, we proposed to produce a global landslide susceptibility map using the method of logistic regression. We find out that topology may not be the first controlling factor of landslides, and finer resolution of DEM may not significantly contribute to the improvement of landslide model when the location precision of landslides is limited.
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