Ranking Sub-Watersheds for Flood Hazard Mapping: A Multi-Criteria Decision-Making Approach

Author:

Nguyen Nguyet-Minh12,Bahramloo Reza3,Sadeghian Jalal4,Sepehri Mehdi5,Nazaripouya Hadi6,Nguyen Dinh Vuong7,Ghahramani Afshin8,Talebi Ali5ORCID,Elkhrachy Ismail9ORCID,Pande Chaitanya B.1011ORCID,Meshram Sarita Gajbhiye12ORCID

Affiliation:

1. Laboratory of Environmental Sciences and Climate Change, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 700000, Vietnam

2. Faculty of Environment, School of Technology, Van Lang University, Ho Chi Minh City 700000, Vietnam

3. Agricultural Engineering Research Department, Hamedan Agricultural and Natural Resources Research and Education Center, AREEO, Hamedan 65199-91169, Iran

4. Department of Civil Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamadan 65187-38695, Iran

5. Department of Watershed Management, Faculty of Natural Resources, Yazd University, Yazd 8915818411, Iran

6. Watershed Research Department, Hamedan Agricultural and Natural Resources Research and Education Center, AREEO, Hamedan 65199-91169, Iran

7. Southern Institute of Water Resources Research, Ho Chi Minh City 700000, Vietnam

8. Institute for Life Sciences and the Environment, Centre for Sustainable Agricultural Systems, University of Southern Queensland, Toowoomba, QLD 4350, Australia

9. Civil Engineering Department, College of Engineering, Najran University, Najran 66291, Saudi Arabia

10. New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Nasiriyah 64001, Iraq

11. Indian Institute of Tropical Meteorology, Pune 411008, Maharashtra, India

12. WRAM Research Lab Pvt. Ltd., Nagpur 440027, India

Abstract

The aim of this paper is to assess the extent to which the Sad-Kalan watershed in Iran participates in floods and rank the Sad-Kalan sub-watersheds in terms of flooding potential by utilizing multi-criteria decision-making approaches. We employed the entropy of a drainage network, stream power index (SPI), slope, topographic control index (TCI), and compactness coefficient (Cc) in this investigation. After forming a decision matrix with 25 possibilities (sub-watersheds) and 5 evaluation indices, we used four MCDM approaches, including the analytic hierarchy process (AHP), best–worst method (BWM), interval rough numbers AHP (IRNAHP), picture fuzzy with AHP (PF-AHP), and picture fuzzy with linear assignment model (PF-LAM, hereafter PICALAM) algorithms, to rank the sub-watersheds. The study results demonstrated that PICALAM exhibited superior performance compared to the other methods due to its consideration of both local and global weights for each criterion. Additionally, among the methods used (AHP, BWM, and IRNAHP) that showed similar performances in ranking the sub-watersheds, the BWM method proved to be more time-efficient in the ranking process.

Funder

Deputy for Research and Innovation Ministry of Education, Kingdom of Saudi Arabia

Publisher

MDPI AG

Subject

Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry

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