Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-183-2025
https://doi.org/10.5194/nhess-25-183-2025
Research article
 | 
07 Jan 2025
Research article |  | 07 Jan 2025

Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models

Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, and Snježana Mihalić Arbanas

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Cited articles

Ballabio, C. and Sterlacchini, S.: Support Vector Machines for Landslide Susceptibility Mapping: The Staffora River Basin Case Study, Italy, Math. Geosci., 44, 47–70, https://doi.org/10.1007/s11004-011-9379-9, 2012. 
Bernat, S., Mihalić Arbanas, S., and Krkač, M.: Landslides triggered in the continental part of Croatia by extreme precipitation in 2013, in: Engineering geology for society and territory, Landslide Process., 2, 1599–1603, 2014a. 
Bernat, S., Mihalić Arbanas, S., and Krkač, M.: Inventory of precipitation triggered landslides in the winter of 2013 in Zagreb (Croatia, Europe), in: Proceedings of the 3rd World Landslide Forum, Landslide Science for a Safer Geoenvironment: Volume 2: Methods of Landslide Studies, 3rd World Landslide Forum, Beijing, China, 2–6 June 2014, 829–836, 2014b 
Bernat Gazibara, S., Krkač, M., Sečanj, M., and Mihalić Arbanas, S.: Identification and Mapping of Shallow Landslides in the City of Zagreb (Croatia) Using the LiDAR–Based Terrain Model, in: Advancing Culture of Living with Landslides, Springer International Publishing, Cham, 1093–1100, https://doi.org/10.1007/978-3-319-53498-5_124, 2017. 
Bernat Gazibara, S., Krkač, M., and Mihalić Arbanas, S.: Verification of historical landslide inventory maps for the Podsljeme area in the City of Zagreb using LiDAR-based landslide inventory, The Mining-Geology-Petroleum Engineering Bulletin, 34, 45–58, https://doi.org/10.17794/rgn.2019.1.5, 2019a. 
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The paper focuses on classifying continuous landslide conditioning factors for susceptibility modelling, which resulted in 54 landslide susceptibility models that tested 11 classification criteria in combination with 5 statistical methods. The novelty of the research is that using stretched landslide conditioning factor values results in models with higher accuracy and that certain statistical methods are more sensitive to the landslide conditioning factor classification criteria than others.
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