Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms

Author:

Arabameri Alireza1ORCID,Chandra Pal Subodh2,Costache Romulus34ORCID,Saha Asish2,Rezaie Fatemeh56ORCID,Seyed Danesh Amir7,Pradhan Biswajeet891011ORCID,Lee Saro56ORCID,Hoang Nhat-Duc12ORCID

Affiliation:

1. Department of Geomorphology, Tarbiat Modares University, Tehran, Iran

2. Department of Geography, The University of Burdwan, Bardhaman, West Bengal, India

3. Research Institute of the University of Bucharest, Bucharest, Romania

4. National Institute of Hydrology and Water Management, Bucharest, Romania

5. Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon, Republic of Korea

6. Department of Geophysical Exploration, Korea University of Science and Technology, Daejeon, Republic of Korea

7. Faculty of Technology and Engineering, East of Guilan, University of Guilan, Rudsar-Vajargah, Iran

8. Faculty of Engineering and Information Technology, Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, Ultimo, NSW, Australia

9. Department of Energy and Mineral Resources Engineering, Sejong University, Seoul, Republic of Korea

10. Department of Meteorology, Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah, Saudi Arabia

11. Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

12. Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, Danang, Vietnam

Funder

Korea Institute of Geoscience and Mineral Resources

Ministry of Science and ICT

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,General Environmental Science

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