New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping

Author:

Costache Romulus12ORCID,Țîncu Roxana3ORCID,Elkhrachy Ismail45ORCID,Pham Quoc Bao67ORCID,Popa Mihnea Cristian89ORCID,Diaconu Daniel Constantin810ORCID,Avand Mohammadtaghi11ORCID,Costache Iulia12ORCID,Arabameri Alireza13ORCID,Bui Dieu Tien14ORCID

Affiliation:

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

2. Flash-Flood Forecast and Warning Department, National Institute of Hydrology and Water Management, Bucharest, Romania

3. Dimitrie Ghika Technical College, Romania

4. College of Engineering, Civil Engineering Department, Najran University, Najran, Saudi Arabia

5. Faculty of Engineering, Civil Engineering Department, Al-Azhar University, Nasr City, Cairo, Egypt

6. Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

7. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

8. Centre for Integrated Analysis and Territorial Management, University of Bucharest, Bucharest, Romania

9. Simion Mehedinți–Nature and Sustainable Development Doctoral School, University of Bucharest, Bucharest, Romania

10. Department of Meteorology and Hydrology, Faculty of Geography, University of Bucharest, Bucharest, Romania

11. Department of Watershed Management Engineering, College of Natural Resources, Tarbiat Modares University, Tehran, Iran

12. Faculty of Geography, University of Bucharest, Bucharest, Romania

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

14. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

Publisher

Informa UK Limited

Subject

Water Science and Technology

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