Landslide susceptibility mapping at sin Ho, Lai Chau province, Vietnam using ensemble models based on fuzzy unordered rules induction algorithm-Reference-Cited by-同舟云学术

Landslide susceptibility mapping at sin Ho, Lai Chau province, Vietnam using ensemble models based on fuzzy unordered rules induction algorithm

Published:2022-10-25 Issue:27 Volume:37 Page:17777-17798
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Bien Tran Xuan¹,Truyen Pham The²³,Phong Tran Van⁴,Nguyen Dam Duc⁵,Amiri Mahdis⁶,Costache Romulus⁷⁸,Duc Dao Minh⁴,Le Hiep Van⁵,Nguyen Hanh Bich Thi⁵,Prakash Indra⁹^ORCID,Pham Binh Thai⁵^ORCID

Affiliation:

1. Hanoi University of Natural Resources and Environment, Hanoi, Vietnam

2. Institute of Geophysics, Vietnam Academy of Science and Technology, Hanoi, Vietnam

3. Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam

4. Institute of Geological Sciences, Vietnam Academy of Science and Technology (VAST), Hanoi, Vietnam

5. University of Transport and Technology, Hanoi, Vietnam

6. Department of Watershed and Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran

7. Danube Delta National Institute for Research and Development, Tulcea, Romania

8. Department of Civil Engineering, Transilvania University of Brasov, Brasov, Romania

9. DDG (R) Geological Survey of India, Gandhinagar, India

Funder

Vietnam National Foundation for Science and Technology Development

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2136253

Reference106 articles.

1. Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain

2. Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia

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

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

5. A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison

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