The influence of cartographic representation on landslide susceptibility models: empirical evidence from a Brazilian UNESCO world heritage site
Author:
Funder
CNPq
FAPEMIG
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11069-024-06576-3.pdf
Reference90 articles.
1. Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon. Indonesia Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006
2. Amato G, Eisank C, Castro-Camilo D, Lombardo L (2019) Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment. Eng Geol 260:105237. https://doi.org/10.1016/j.enggeo.2019.105237
3. Barella CF, Sobreira FG, Zêzere JL (2019) A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil. Bull Eng Geol Environ 78:3205–3221. https://doi.org/10.1007/s10064-018-1341-3
4. Biswakarma P, Joshi V, Abdo HG et al (2023) An integrated quantitative and qualitative approach for landslide susceptibility mapping in West Sikkim district, Indian Himalaya. Geomat Nat Hazards Risk 14. https://doi.org/10.1080/19475705.2023.2273781
5. Blahut J, van Westen CJ, Sterlacchini S (2010) Analysis of landslide inventories for accurate prediction of debris-flow source areas. Geomorphology 119:36–51. https://doi.org/10.1016/j.geomorph.2010.02.017
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