Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes-Reference-Cited by-同舟云学术

Predicting landslide susceptibility based on decision tree machine learning models under climate and land use changes

Published:2021-10-18 Issue: Volume: Page:1-27
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Pham Quoc Bao¹^ORCID,Chandra Pal Subodh²^ORCID,Chakrabortty Rabin²^ORCID,Saha Asish²^ORCID,Janizadeh Saeid³,Ahmadi Kourosh⁴,Khedher Khaled Mohamed⁵⁶,Anh Duong Tran⁷,Tiefenbacher John P.⁸^ORCID,Bannari Abderrazak⁹

Affiliation:

1. Institute of Applied Technology, Thu Dau Mot University, Thu Dau Mot City, Vietnam

2. Department of Geography, The University of Burdwan, Bardhaman, India

3. Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran, Iran

4. Department of Forestry, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, Tehran, Iran

5. Department of Civil Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia

6. Department of Civil Engineering, High Institute of Technological Studies, Mrezgua University Campus, Nabeul, Tunisia

7. Institute of Applied Science, Ho Chi Minh City University of Technology (HUTECH) 475A, Ho Chi Minh City, Vietnam;

8. Department of Geography, Texas State University, San Marcos, Texas, USA

9. Space Pix-Map International Inc., Gatineau, Quebec, Canada

Funder

Deanship of Scientific Research at King Khalid University

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2021.1986579

Reference71 articles.

1. Modeling of gas hydrate phase equilibria: Extremely randomized trees and LSSVM approaches

2. Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility

3. The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models

4. Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

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