Remote Sensing and GIS-Based Landslide Susceptibility Mapping in a Hilly District of Bangladesh: A Comparison of Different Geospatial Models
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12524-024-01988-x.pdf
Reference89 articles.
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2. Abeysiriwardana, H. D., & Gomes, P. I. (2022). Integrating vegetation indices and geo-environmental factors in GIS-based landslide-susceptibility mapping: Using logistic regression. Journal of Mountain Science, 19(2), 477–492. https://doi.org/10.1007/s11629-021-6988-8
3. Acharyya, S. (2015). Indo-Burma range: A belt of accreted microcontinents, ophiolites and Mesozoic-Paleogene flyschoid sediments. International Journal of Earth Sciences, 104(5), 1235–1251. https://doi.org/10.1007/s00531-015-1154-6
4. Adnan, M. S. G., Rahman, M. S., Ahmed, N., Ahmed, B., Rabbi, M. F., & Rahman, R. M. (2020). Improving spatial agreement in machine learning-based landslide susceptibility mapping. Remote Sensing, 12(20), 20. https://doi.org/10.3390/rs12203347
5. Ahmed, B. (2015). Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides, 12(6), 1077–1095. https://doi.org/10.1007/s10346-014-0521-x
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