Determining Land Induced Factors for Landslide Susceptibility in Indian Cities
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-56591-5_9
Reference83 articles.
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2. Achu AL, Aju CD, Di Napoli M, Prakash P, Gopinath G, Shaji E, Chandra V (2023) Machine-learning based landslide susceptibility modeling with emphasis on uncertainty analysis. Geosci Front 101657. https://doi.org/10.1016/j.gsf.2023.101657
3. Addis A (2023) olGIS—based flood susceptibility mapping using frequency ratio and information value models in upper Abay river basin, Ethiopia. Nat Haz Res. https://doi.org/10.1016/j.nhres.2023.02.003
4. Ali SA, Parvin F, Vojteková J, Costache R, Linh NTT, Pham QB, Vojtek M, Gigović L, Ahmad A, Ghorbani MA (2021) GIS-based landslide susceptibility modeling: a comparison between fuzzy multi-criteria and machine learning algorithms. Geosci Front 12(2):857–876. https://doi.org/10.1016/j.gsf.2020.09.004
5. Alsabhan AH, Singh K, Sharma A, Alam S, Pandey DD, Rahman SAS, Khursheed A, 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). https://doi.org/10.1016/j.jksus.2021.101759
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