Community-Driven Risk Assessment: Integrating Local Perceptions into Quantifiable Risk Weights Using Analytical Hierarchy Process (AHP)-Geographical Information System (GIS)

Authors

  • Biswojit Kumar Roy Disaster Management Specialist, Dhaka, Bangladesh
  • Md. Islamul Haque Shawon Development Project Design & Services Ltd, Dhaka, Bangladesh
  • Mohammad Mahdy Hasan Anthropologist, BRAC, Dhaka, Bangladesh

DOI:

https://doi.org/10.18485/ijdrm.2025.7.2.8

Keywords:

analytical hierarchy process (AHP), community risk mapping, hazard index and vulnerability index, remote sensing, transect walk, historical timeline, seasonal calendar, focus group discussion (FGD), key informant interview (KII), participatory rural appraisal (PRA), geographic information system (GIS)

Abstract

This study integrates the Analytical Hierarchy Process (AHP) and Geographic Information System (GIS) to assess community flood risk in Maidipur, Kurigram, Bangladesh an area frequently affected by monsoon flooding. By combining community-based hazard and vulnerability indicators and satellite-derived hydrological, meteorological, and geological data, the research develops a Community Risk Map to identify and categorize hazard-prone zones. Fourteen indicators were ranked and weighted using AHP to generate hazard and vulnerability indices, which were then spatially overlaid in GIS to produce a composite flood risk map. Network also applied to identify optimal evacuation routes and assembly points based on shortest time travel and distance, avoid risk zone. Traditionally, practitioners relied on community perception and a limited set of factors for risk mapping. This study advances that approach by aligning local knowledge with scientific analysis, achieving a high accuracy rate (96.88%) validated through field visits and community engagement... The findings offer evidence- based insights for flood preparedness, shelter placement, and community resilience planning under the Disaster Risk Management framework.  

References

1. Abdel Rahman Al-Shabeeb, R. A.-A. (2016). AHP with GIS for a preliminary site selection of wind turbines in the Northwest of Jordan. International Journal of Geosciences, 7(10), 1208–1221. https://doi.org/10.4236/ijg.2016.710090

2. Abdulla-Al Kafy, A.-A.-F. M. (2020). Impact of LULC changes on LST in Rajshahi District of Bangladesh: A remote sensing approach. Journal of Geographical Studies, 3(1), 11–23. https://doi.org/10.21523/gcj5.19030102

3. Abhishek Banerjee, R. C. (2020). An analysis of long-term rainfall trends and variability in the Uttarakhand Himalaya using Google Earth Engine. Remote Sensing, 12(4), 709. https://doi.org/10.3390/rs12040709

4. Borah, P. B., Das, S., & Hazarika, R. (2024). Flood risk assessment utilizing GIS-based AHP and MCDM approaches integrating local knowledge and community perception. Natural Hazards. https://doi.org/10.1007/s11069-024-07100-3

5. Chambers, R. (1994). Participatory rural appraisal (PRA): Analysis of experience. World Development, 22(9), 1253–1268.

6. Dang Tuyet Minh, N. B. (2018). Application of GIS technology to establish a drainage density hierarchical map for flood hazard zoning in Lam river basin. Journal of Mining and Earth Sciences, 59(6), 32–42. Retrieved from https://tapchi.humg.edu.vn/en/archives?article=1068

7. Daniela Rincon, U. T. (2018). Flood risk mapping using GIS and multi-criteria analysis: A Greater Toronto Area case study. Geosciences, 8(8), 275. https://doi.org/10.3390/geosciences8080275

8. Diego Alonso Arias-Choquehuanca, B. I.-N.-J. (2023). Flooding mapping detection and urban affectation using Google Earth Engine. Dyna, 90(229), 129–136. https://doi.org/10.15446/dyna.v90n229.109296

9. Elhadj Mokhtari, F. M. (2023). Flood risk assessment using analytical hierarchy process: A case study from the Cheliff-Ghrib watershed, Algeria. Journal of Water and Climate Change, 14(3), 694–711. https://doi.org/10.2166/wcc.2023.316

10. Ethiopia Bisrat, B. B. (2018). Identification of surface water storing sites using topographic wetness index (TWI) and normalized difference vegetation index (NDVI). Journal of Natural Resources and Development, 8, 91–100. https://doi.org/10.5027/jnrd.v8i0.09

11. Generino P. Siddayao, S. E. (2014). Analytic hierarchy process (AHP) in spatial modeling for floodplain risk assessment. International Journal of Machine Learning and Computing, 4(5), 2010–3700. https://doi.org/10.7763/IJMLC.2014.V4.453

12. Gowhar Meraj, S. A. (2015). Assessing the influence of watershed characteristics on the flood vulnerability of Jhelum basin in Kashmir Himalaya. Natural Hazards, 77(13), 153–175. https://doi.org/10.1007/s11069-015-1605-1

13. Hye-Kyoung Lee, W.-H. H.-H. (2019). Experimental study on the influence of water depth on the evacuation speed of elderly people in flood conditions. International Journal of Disaster Risk Reduction, 39, 101198. https://doi.org/10.1016/j.ijdrr.2019.101198

14. Kabir Uddin, M. A. (2021). Potential flood hazard zonation and flood shelter suitability mapping for disaster risk mitigation in Bangladesh using geospatial technology. Progress in Disaster Science, 11, 100185. https://doi.org/10.1016/j.pdisas.2021.100185

15. Melkamu Alebachew Anley, A. S. (2022). Assessing the impacts of land use/cover changes on ecosystem service values in Rib watershed, Upper Blue Nile Basin, Ethiopia. Trees, Forests and People, 7, 100212. https://doi.org/10.1016/j.tfp.2022.100212

16. Muhammad Masood, K. T. (2012). Assessment of flood hazard, vulnerability and risk of mid-eastern Dhaka using DEM and 1D hydrodynamic model. Natural Hazards, 12, 757–770. https://doi.org/10.1007/s11069-011-0060-x

17. Nsangou, D. (2022). Urban flood susceptibility modelling using AHP and GIS approach: Case of the Mfoundi watershed at Yaoundé in the South-Cameroon plateau. Scientific African, 15, 1043. https://doi.org/10.1016/j.sciaf.2021.e01043

18. Peter W Glynn, J. M. (2001). Coral bleaching and mortality in Panama and Ecuador during the 1997–1998 El Niño-Southern Oscillation event: Spatial/temporal patterns and comparisons with the 1982–1983 event. Bulletin of Marine Science, 79, 79–109. Retrieved from https://www.researchgate.net/publication/233572646

19. Ravichandra N, Y. P. (2023). Change detection of NDVI in Sangareddy district using Google Earth Engine (GEE). The Pharma Innovation Journal, 12(11), 2189–2195. Retrieved from https://www.thepharmajournal.com/archives/?year=2023&vol=12&issue=11&ArticleId=24334

20. Robert C. Weih Jr, T. L. (2004). Modeling slope in a geographic information system. Journal of the Arkansas Academy of Science, 58, 18. Retrieved from https://scholarworks.uark.edu/jaas/vol58/iss1/18

21. Saaty, R. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8

22. Seula Park, G. L. (2020). Flood evacuation mapping using a time–distance cartogram. International Journal of Geo-Information, 9(4), 207. https://doi.org/10.3390/ijgi9040207

23. Yashon O. Ouma, R. T. (2014). Urban flood vulnerability and risk mapping using integrated multi-parametric AHP and GIS: Methodological overview and case study assessment. Water, 6(6), 1515–1545. https://doi.org/10.3390/w6061515.

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Published

2025-12-24

How to Cite

Roy, B. K., Shawon, M. I. H., & Hasan, M. M. (2025). Community-Driven Risk Assessment: Integrating Local Perceptions into Quantifiable Risk Weights Using Analytical Hierarchy Process (AHP)-Geographical Information System (GIS). International Journal of Disaster Risk Management, 7(2), 131–152. https://doi.org/10.18485/ijdrm.2025.7.2.8

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