A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping-Reference-Cited by-同舟云学术

A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping

Published:2021-01-01 Issue:1 Volume:12 Page:1741-1777
ISSN:1947-5705
Container-title:Geomatics, Natural Hazards and Risk
language:en
Short-container-title:Geomatics, Natural Hazards and Risk

Author:

Pham Quoc Bao¹^ORCID,Achour Yacine²^ORCID,Ali Sk Ajim³^ORCID,Parvin Farhana³^ORCID,Vojtek Matej⁴^ORCID,Vojteková Jana⁴^ORCID,Al-Ansari Nadhir⁵,Achu A. L.⁶^ORCID,Costache Romulus⁷,Khedher Khaled Mohamed⁸⁹,Anh Duong Tran¹⁰^ORCID

Affiliation:

1. Institute of Applied Technology, Thu Dau Mot University, Binh Duong province, Viet Nam

2. Department of Civil Engineering, Bordj Bou Arreridj University, El Annasser, Algeria

3. Faculty of Science, Department of Geography, Aligarh Muslim University, Aligarh, India

4. Department of Geography and Regional Development, Faculty of Natural Sciences, Constantine the Philosopher University in Nitra, Nitra, Slovakia

5. Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden

6. Department of Remote Sensing and GIS, Kerala University of Fisheries and Ocean Studies, Kochi, India

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

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

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

10. Ho Chi Minh City University of Technology (HUTECH) 475A, Ho Chi Minh City, Vietnam

Funder

Slovak Research and Development Agency

KEGA agency

the Deanship of Scientific Research at King Khalid University

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2021.1944330

Reference93 articles.

1. GIS-based spatial prediction of debris flows using logistic regression and frequency ratio models for Zêzere River basin and its surrounding area, Northwest Covilhã, Portugal

2. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

3. Spatial modelling of shallow landslide susceptibility: a study from the southern Western Ghats region of Kerala, India.

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

5. An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm

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