Assessing the performance of MCDM, statistical and machine learning ensemble models for gully sensitivity mapping in a semi-arid context-Reference-Cited by-同舟云学术

Assessing the performance of MCDM, statistical and machine learning ensemble models for gully sensitivity mapping in a semi-arid context

Published:2022-10-19 Issue:27 Volume:37 Page:17435-17464
ISSN:1010-6049
Container-title:Geocarto International
language:en
Short-container-title:Geocarto International

Author:

Eloudi Hasna¹,Reddad Hanane²,Hssaisoune Mohammed¹³,Estrany Joan⁴,Krimissa Samira⁵,Elaloui Abdenbi⁵,Namous Mustapha⁵,Ouatiki Hamza⁵⁶,Aboutaib Fatima⁵,Ouayah Mustapha⁵,Jadoud Mourad⁷,Edahbi Mohamed²,Bouchaou Lhoussaine¹⁸

Affiliation:

1. Applied Geology and Geo-Environment Laboratory, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco

2. Higher School of Technology (ESTBM), Sultan Moulay Slimane University, Beni Mellal, Morocco

3. Faculty of Applied Sciences, Ibn Zohr University, Ait Melloul, Morocco

4. Mediterranean Ecogeomorphological and Hydrological Connectivity Research Team, partamento de Geografia, Universitat de les Illes Balears, Palma, Spain

5. Data Science for Sustainable Earth Laboratory (Data4Earth), Sultan Moulay Slimane University, Beni-Mellal, Morocco

6. Center for Remote Sensing Application (CRSA), Mohammed VI Polytechnic University, Ben Guerir, Morocco

7. Laboratory Geosciences and Environmental Techniques, Faculty of Sciences El Jadida, Chouaïb Doukkali University, El Jadida, Morocco

8. International Water Research Institute, Mohammed VI Polytechnic University, Ben Guerir, Morocco

Funder

the CHARISMA Project

the Hassan 2 Academy of Sciences and techniques

Ibn Zohr University, WASA project and Institut de Recherche pour le Développement

Publisher

Informa UK Limited

Subject

Water Science and Technology,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10106049.2022.2129818

Reference62 articles.

1. Multi-criteria decision analysis for delineation of groundwater potential zones in a tropical river basin using remote sensing, GIS and analytical hierarchy process (AHP)

2. Validation of TRMM Radar Rainfall Data over Major Climatic Regions in Africa

3. Susceptibility to Gully Erosion: Applying Random Forest (RF) and Frequency Ratio (FR) Approaches to a Small Catchment in Ethiopia

4. Comparison of machine learning models for gully erosion susceptibility mapping

5. Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS

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