Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers-Reference-Cited by-同舟云学术

Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers

Published:2020-03-13 Issue: Volume: Page:1-23
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Pham Binh Thai¹²^ORCID,Phong Tran Van³,Nguyen-Thoi Trung¹²^ORCID,Parial Kajori⁴,K. Singh Sushant⁵,Ly Hai-Bang⁶^ORCID,Nguyen Kien Trung³,Ho Lanh Si⁷,Le Hiep Van⁶,Prakash Indra⁸^ORCID

Affiliation:

1. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam;

2. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam;

3. Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, Hanoi, Viet Nam;

4. West Bengal State Council of Science and Technology, Department of Science and Technology and Biotechnology, Government of West Bengal, Kolkata, India;

5. Virtusa Corporation, New York City, NY, USA;

6. University of Transport Technology, Viet Nam;

7. Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam;

8. Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, India

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2020.1737972

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5. Application of artificial neural networks for predicting tree survival and mortality in the Hyrcanian forest of Iran

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