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Ranking Sub-Watersheds for Flood Hazard Mapping:A Multi-Criteria Decision-Making Approach
Journal
Water
ISSN
2073-4441
Date Issued
2023
Author(s)
Nguyet Minh Nguyen, Reza Bahramloo, Jalal Sadeghian, Mehdi Sepehri, Hadi Nazaripouya, Vuong Nguyen Dinh, Afshin Ghahramani, Ali Talebi, Ismail Elkhrachy, Chaitanya B. Pande, Sarita Gajbhiye Meshram.
DOI
https://doi.org/10.3390/w15112128
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."
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."
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