Skip to content
DraconiticResearch
Search Sources API
197,357 studies31 sourcesUpdated 25 min ago
Peer-reviewedCC BYDiscover Environment

Application of the fuzzy analytical hierarchy process for flood vulnerability assessment in Sylhet District, Bangladesh

4 affiliations
  1. 1Environmental Science and Disaster Management, Gopalganj Science and Technology University
  2. 2Institute of Water and Environment, Dhaka University of Engineering and Technology, Gazipur
  3. 3Department of Horticulture, Gazipur Agricultural University, Gazipur
  4. 4Department of Environmental Science, Gazipur Agricultural University, Gazipur

Abstract

Floods are among the most frequent and destructive natural hazards, and their frequency and intensity have increased under recent climatic and environmental changes. Sylhet District is one of the most flood-vulnerable regions of Bangladesh, where riparian and low-lying communities face recurrent flood-related challenges. This study assesses flood vulnerability in Sylhet District using the Fuzzy analytical hierarchy process (Fuzzy AHP), a multi-criteria decision-making technique that improves conventional AHP by incorporating uncertainty and ambiguity in expert judgment. Sixteen physical flood-conditioning indicators, including rainfall, elevation, slope, drainage density, land use and land cover, geology, flow accumulation, soil texture, hydrological soil group, topographic wetness index, permeability, curvature, water table depth, stream power index, distance from river, and normalized difference vegetation index, were integrated and weighted within a geographic information system environment. The consistency of the Fuzzy AHP pairwise comparison matrix was assessed using the Consistency Ratio, which was 0.0592, indicating acceptable consistency as it is below the threshold of 0.10. The generated flood vulnerability map categorized the district into four vulnerability levels: low, moderate, high, and very high. The findings reveal that 149.508 km2, representing 4.48% of the study area, falls within the low vulnerability zone, while 597.884 km2, accounting for 17.90%, falls within the moderate vulnerability zone. The high vulnerability class occupied the largest area, covering 1306.765 km2 or 39.12% of the district, while the very high vulnerability class covered 1285.870 km2 or 38.50%. Overall, high and very high vulnerability zones together accounted for 77.62% of Sylhet District, indicating widespread flood susceptibility across the study area. The final vulnerability map was validated using Receiver Operating Characteristic-Area Under the Curve analysis, producing an AUC value of 0.809, which indicates very good predictive performance. This spatial assessment provides critical insights for disaster preparedness, targeted flood mitigation, drainage improvement, land-use planning, wetland conservation, and evidence-based decision-making for sustainable regional development.

Related

  1. Landslide susceptibility mapping using machine learning models and analytical hierarchy process along NH-513, Pasighat to Yingkiong, Arunachal Pradesh, India
    Mrinaljyoti Adhyapok, Mrinal Kumar Dutta
    Discover GeosciencePeer-reviewedCC BYFull textEnvironmental science & pollutionDOAJ
  2. Application of radar remote sensing for cyclone damage mapping in Bangladesh: a coherence-based approach
    Shadmin Islam Prova, Md. Shahoriar Sarker, Rosdania Islam +1
    Natural Hazards and Earth System SciencesPeer-reviewedCC BY 4.0Full textGeophysics & seismologyCrossref
  3. Assessment of tropical cyclone vulnerability of coastal communities in Mtwara Municipal Council, Tanzania: a Composite Vulnerability Index approach
    Denis S. Ndenzako, Denis S. Ndenzako, Noah Makula Pauline +4
    Frontiers in ClimatePeer-reviewedCC BYFull textEnvironmental science & pollutionDOAJ
  4. Delineating season-specific hotspots: A spatio-temporal risk assessment of neck blast disease in rainfed and irrigated ecosystems of Cumilla District, Bangladesh
    Md. Mamunur Rashid, Niaz Md. Farhat Rahman, Nazmul Haque +8
    PLOS OnePeer-reviewedCC BY 4.0Full textEcologyPlant biologyPLOS
  5. Green space dynamics in a rapidly urbanizing landscape: A multi-temporal remote sensing assessment and CA-Markov modelling of Jashore, Bangladesh
    Kazi Md Arifin Jamil Jany, Abu Nayem Md. Kayes, Md. Kamrul Hasan
    Ecological IndicatorsPeer-reviewedCC BYFull textClimate scienceDOAJ
BibTeX
@article{kundu2026application,
  title = {{Application of the fuzzy analytical hierarchy process for flood vulnerability assessment in Sylhet District, Bangladesh}},
  author = {Susmita Kundu and Md. Biplob Hosen and Samsunnahar Popy and Umme Tahera Tun Humayra and Md. Mostafijar Rahman and Md. Salauddin and Md. Tamal Hasan},
  journal = {Discover Environment},
  year = {2026},
  month = oct,
  doi = {10.1007/s44274-026-01031-5},
  url = {https://doi.org/10.1007/s44274-026-01031-5},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Application of the fuzzy analytical hierarchy process for flood vulnerability assessment in Sylhet District, Bangladesh
AU  - Susmita Kundu
AU  - Md. Biplob Hosen
AU  - Samsunnahar Popy
AU  - Umme Tahera Tun Humayra
AU  - Md. Mostafijar Rahman
AU  - Md. Salauddin
AU  - Md. Tamal Hasan
PY  - 2026
DA  - 2026/10/25
JO  - Discover Environment
DO  - 10.1007/s44274-026-01031-5
UR  - https://doi.org/10.1007/s44274-026-01031-5
AB  - Floods are among the most frequent and destructive natural hazards, and their frequency and intensity have increased under recent climatic and environmental changes. Sylhet District is one of the most flood-vulnerable regions of Bangladesh, where riparian and low-lying communities face recurrent flood-related challenges. This study assesses flood vulnerability in Sylhet District using the Fuzzy analytical hierarchy process (Fuzzy AHP), a multi-criteria decision-making technique that improves conventional AHP by incorporating uncertainty and ambiguity in expert judgment. Sixteen physical flood-conditioning indicators, including rainfall, elevation, slope, drainage density, land use and land cover, geology, flow accumulation, soil texture, hydrological soil group, topographic wetness index, permeability, curvature, water table depth, stream power index, distance from river, and normalized difference vegetation index, were integrated and weighted within a geographic information system environment. The consistency of the Fuzzy AHP pairwise comparison matrix was assessed using the Consistency Ratio, which was 0.0592, indicating acceptable consistency as it is below the threshold of 0.10. The generated flood vulnerability map categorized the district into four vulnerability levels: low, moderate, high, and very high. The findings reveal that 149.508 km2, representing 4.48% of the study area, falls within the low vulnerability zone, while 597.884 km2, accounting for 17.90%, falls within the moderate vulnerability zone. The high vulnerability class occupied the largest area, covering 1306.765 km2 or 39.12% of the district, while the very high vulnerability class covered 1285.870 km2 or 38.50%. Overall, high and very high vulnerability zones together accounted for 77.62% of Sylhet District, indicating widespread flood susceptibility across the study area. The final vulnerability map was validated using Receiver Operating Characteristic-Area Under the Curve analysis, producing an AUC value of 0.809, which indicates very good predictive performance. This spatial assessment provides critical insights for disaster preparedness, targeted flood mitigation, drainage improvement, land-use planning, wetland conservation, and evidence-based decision-making for sustainable regional development.
SN  - 2731-9431
ER  -