Landslide susceptibility mapping in Badakhshan province, Afghanistan: a comparative study of machine learning algorithms-Reference-Cited by-同舟云学术

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Landslide susceptibility mapping in Badakhshan province, Afghanistan: a comparative study of machine learning algorithms

Published:2023-08-23 Issue:1 Volume:38 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Qasimi Abdul Baser¹^ORCID,Isazade Vahid²^ORCID,Enayat Enayatullah³,Nadry Zabihullah¹,Majidi Abdul Hallim⁴

Affiliation:

1. Department of Geography, Education Faculty, Samangan University, Aibak, Afghanistan

2. Department of Geographical Science, Kharazmi University, Tehran, Iran

3. Department of Maths, Education Faculty, Samangan University, Aibak, Afghanistan

4. Department of Biology, Education Faculty, Badakhshan University, Faizabad, Afghanistan

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2023.2248082

Reference83 articles.

1. Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey

2. Application of an evidential belief function model in landslide susceptibility mapping

3. An ensemble model for landslide susceptibility mapping in a forested area

4. GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms

5. Volumetric Analysis of the Landslide in Abe Barek, Afghanistan Based on Nonlinear Mapping of Stereo Satellite Imagery-Derived DEMs

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3. Assessing the relationship between landslide susceptibility and land cover change using machine learning;Vietnam Journal of Earth Sciences;2024-05-02

4. Integrating Remote Sensing, GIS and Machine Learning Approaches in Evaluation of Landslide Susceptibility in Mountainous Region of Nghe An Province, Vietnam;IOP Conference Series: Earth and Environmental Science;2024-05-01

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