National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models : a case of Bangladesh
Siam, Zakaria Shams and Hasan, Rubyat Tasnuva and Anik, Soumik Sarker and Noor, Fahima and Adnan, Mohammed Sarfaraz Gani and Rahman, Rashedur M. and Dewan, Ashraf (2022) National-scale flood risk assessment using GIS and remote sensing-based hybridized deep neural network and fuzzy analytic hierarchy process models : a case of Bangladesh. Geocarto International, 37 (26). pp. 12119-12148. ISSN 1010-6049 (https://doi.org/10.1080/10106049.2022.2063411)
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Abstract
Assessing flood risk is challenging due to complex interactions among flood susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel flood risk assessment framework by utilizing a hybridized deep neural network (DNN) and fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as a case study region, where limited studies examined flood risk at a national scale. The results exhibited that hybridized DNN and fuzzy AHP models can produce the most accurate flood risk map while comparing among 15 different models. About 20.45% of Bangladesh are at flood risk zones of moderate, high, and very high severity. The northeastern region, as well as areas adjacent to the Ganges–Brahmaputra–Meghna rivers, have high flood damage potential, where a significant number of people were affected during the 2020 flood event. The risk assessment framework developed in this study would help policymakers formulate a comprehensive flood risk management system.
ORCID iDs
Siam, Zakaria Shams, Hasan, Rubyat Tasnuva, Anik, Soumik Sarker, Noor, Fahima, Adnan, Mohammed Sarfaraz Gani ORCID: https://orcid.org/0000-0002-7276-1891, Rahman, Rashedur M. and Dewan, Ashraf;-
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Item type: Article ID code: 86084 Dates: DateEvent2022Published25 April 2022Published Online3 April 2022AcceptedSubjects: Geography. Anthropology. Recreation > Physical geography > Hydrology. Water
Geography. Anthropology. Recreation > Environmental SciencesDepartment: Faculty of Engineering > Civil and Environmental Engineering Depositing user: Pure Administrator Date deposited: 10 Jul 2023 09:42 Last modified: 04 Nov 2024 01:52 URI: https://strathprints.strath.ac.uk/id/eprint/86084