Evaluating causative factors for landslide susceptibility along the Imphal-Jiribam railway corridor in the North-Eastern part of India using a GIS-based statistical approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11356-023-28966-z.pdf
Reference74 articles.
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2. Al-Najjar HA, Kalantar B, Pradhan B, Saeidi V (2019) Conditioning factor determination for mapping and prediction of landslide susceptibility using machine learning algorithms. Earth resources and environmental remote sensing/GIS applications X 11156:97–107. SPIE. https://doi.org/10.1117/12.2532687
3. Alsabhan AH, Singh K, Sharma A, Alam S, Pandey DD, Rahman SAS, Munshi FM (2022) Landslide susceptibility assessment in the Himalayan range based along Kasauli–Parwanoo road corridor using weight of evidence, information value, and frequency ratio. J King Saud Univ-Sci 34(2):101759. https://doi.org/10.1016/j.jksus.2021.101759
4. Azarafza M, Azarafza M, Akgün H, Atkinson PM, Derakhshani R (2021) Deep learning-based landslide susceptibility mapping. Sci Rep 11(1):24112. https://doi.org/10.1038/s41598-021-03585-1
5. Balamurugan G, Ramesh V, Touthang M (2016) Landslide susceptibility zonation mapping using frequency ratio and fuzzy gamma operator models in part of NH-39, Manipur, India. Nat Hazards 84:465–488. https://doi.org/10.1007/s11069-016-2434-6
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