Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Link
https://link.springer.com/content/pdf/10.1007/s11069-021-04743-4.pdf
Reference93 articles.
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2. Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9(1):93–106. https://doi.org/10.1007/s10346-011-0283-7
3. Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135. https://doi.org/10.1016/j.cageo.2012.03.003
4. Arabameri A, Saha S, Roy J, Chen W, Blaschke T, Bui DT (2020) Landslide susceptibility evaluation and management using different machine learning methods in The Gallicash River Watershed, Iran. Remote Sens 12(3):475. https://doi.org/10.3390/rs12030475
5. Bai S-B, Lu P, Wang J (2015) Landslide susceptibility assessment of the Youfang catchment using logistic regression. J Mt Sci 12(4):816–827. https://doi.org/10.1007/s11629-014-3171-5
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