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Mapping flood-prone areas’ sensitivity to extreme rainfall is crucial for disaster risk reduction. Using the analytical hierarchy process (AHP) and fuzzy AHP methods with Geographic Information Systems (GIS) data, including factors like slope, elevation, precipitation, land use, and vegetation indices, enhances disaster preparedness. This approach enables creating maps that support informed decision-making for community resilience. In this study, AHP and fuzzy AHP models were applied across different return periods (2, 5, 10, 100, and 1,000 years) and average annual rainfall, revealing that high-risk areas vary by model: the AHP model showed high susceptibility in 2.19–25.55% of the area, while the fuzzy AHP model identified 1.53–24.51% as very sensitive. These risks are concentrated in the low-slope zones of the Boussellam and K'sob watersheds, posing a threat to three out of four nearby cities for 100-year or longer events.

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  • This study applies both AHP and fuzzy AHP to improve flood sensitivity analysis precision.

  • Using GIS, it integrates dataset for detailed flood-prone area analysis.

  • It produces maps for multiple return periods and annual precipitation, aiding proactive planning.

  • ROC curves validate model effectiveness in predicting flood-prone areas.

  • It highlights flood-sensitive zones in Boussellam and K'sob watersheds.

AHP, Boussellam and K'sob watersheds, extreme rainfall, flood, fuzzy AHP, GIS, sensitivity
© 2025 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).