Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis: Evidence from Shimla district of North-west Indian Himalayan region-Reference-Cited by-同舟云学术

Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis: Evidence from Shimla district of North-west Indian Himalayan region

Published:2024-07 Issue:7 Volume:21 Page:2368-2393
ISSN:1672-6316
Container-title:Journal of Mountain Science
language:en
Short-container-title:J. Mt. Sci.

Author:

Sharma Aastha^ORCID,Sajjad Haroon^ORCID,Rahaman Md Hibjur^ORCID,Saha Tamal Kanti^ORCID,Bhuyan Nirsobha^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11629-024-8651-7.pdf

Reference78 articles.

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2. Ado M, Amitab K, Maji AK, et al. (2022) Landslide susceptibility mapping using machine learning: A literature survey. Remote Sens 14. https://doi.org/10.3390/rs14133029

3. Ali R, Sajjad H, Saha TK, et al. (2023) Effectiveness of machine learning ensemble models in assessing groundwater potential in Lidder watershed, India. Acta Geophys. https://doi.org/10.1007/s11600-023-01237-8

4. Alqadhi S, Mallick J, Talukdar S, et al. (2022) Selecting optimal conditioning parameters for landslide susceptibility: an experimental research on Aqabat Al-Sulbat, Saudi Arabia. Environ Sci Pollut Res 29:3743–3762. https://doi.org/10.1007/s11356-021-15886-z

5. Anand V, Sharma A, Sahni AK, et al. (2022) Landslide susceptibility mapping using Shannon’s entropy methods using hybrid technique: A case study of Kinnaur District, Himachal Pradesh, India. J Remote Sens GIS. https://doi.org/10.35248/2469-4134.22.11.261