Deep learning to assess the effects of land use/land cover and climate change on landslide susceptibility in the Tra Khuc river basin of Vietnam-Reference-Cited by-同舟云学术

Deep learning to assess the effects of land use/land cover and climate change on landslide susceptibility in the Tra Khuc river basin of Vietnam

Published:2023-01-31 Issue:1 Volume:38 Page:
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Viet Du Quan Vu¹,Nguyen Huu Duy¹,Pham Viet Thanh¹,Nguyen Cao Huan¹,Nguyen Quoc-Huy²,Bui Quang-Thanh¹,Doan Thanh Thuy³,Tran Anh Tuan⁴,Petrisor Alexandru-Ionut⁵⁶⁷^ORCID

Affiliation:

1. Faculty of Geography, University of Science, Vietnam National University, Hanoi, Vietnam

2. Centre for Applied Research in Remote Sensing and GIS, Faculty of Geography, University of Science, Vietnam National University, Hanoi, Vietnam

3. Faculty of Natural Resources and Environment, Vietnam National University of Agriculture, Hanoi, Vietnam

4. Vietnam Ministry of Education and Training, Hanoi, Vietnam

5. Doctoral School of Urban Planning, Ion Mincu University of Architecture and Urbanism, Bucharest, Romania

6. National Institute for Research and Development in Tourism, Bucharest, Romania

7. National Institute for Research and Development in Constructions, Urbanism and Sustainable Spatial Development URBAN-INCERC, Bucharest, Romania

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2023.2172218

Reference125 articles.

1. Landslide Susceptibility Mapping Using GIS-based Fuzzy Logic and the Analytical Hierarchical Processes Approach: A Case Study in Constantine (North-East Algeria)

2. Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria

3. A novel hybrid dragonfly optimization algorithm for agricultural drought prediction

4. Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

5. GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms

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