Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology-Reference-Cited by-同舟云学术

Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology

Published:2024-01-10 Issue:7 Volume:31 Page:10443-10459
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Saha Abhik,Tripathi Lakshya,Villuri Vasanta Govind Kumar,Bhardwaj Ashutosh

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11356-023-31670-7.pdf

Reference69 articles.

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2. Ahmed B (2015) Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides 12:1077–1095. https://doi.org/10.1007/s10346-014-0521-x

3. Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Env 58:21–44. https://doi.org/10.1007/s100640050066

4. Arabameri A, Pourghasemi HR, Yamani M (2017) Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environ Earth Sci 76:1–20. https://doi.org/10.1007/s12665-017-7177-5

5. Arabameri A, Pradhan B, Rezaei K (2019a) Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. J Environ Manag 232:928–942. https://doi.org/10.1016/j.jenvman.2018.11.110

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