A GIS–based fuzzy decision model for gender-responsive flood vulnerability assessment in data-scarce environments

Abstract

This study pioneers a gender-responsive framework for flood vulnerability assessment in Oluyole LGA, Oyo State, Nigeria, integrating a two-stage Pentagonal Fuzzy Analytic Hierarchy Process (PFAHP) with remote sensing and GIS. Physical indicators, derived from satellite data (e.g., elevation, slope, high-resolution land use land cover) and ancillary datasets (soils, rainfall, drainage), were combined with Gender responsive social indicators, including maternal health, female education, and safe space access. The two-stage PFAHP weighted indicators were used to ensure environmental factors did not overshadow gender-related vulnerabilities. PFAHP improves traditional AHP by better handling uncertainty in decision-making. The resulting Flood Vulnerability Index (FVI) identified rural southern wards (Araromi, Latunde, Idi Ayunre) as most at risk due to low-lying terrain and limited healthcare, while urban wards (Olomi, Idi-Iroko) showed moderate vulnerability from high population density. Validated against a DEM-based flood simulation with 93.7% spatial correspondence, the model proves reliable despite data scarcity. By integrating gender equity into flood risk mapping, this study advances disaster risk reduction, supporting Sendai Framework priorities and SDGs 5 (Gender Equality) and 13 (Climate Action). Recommendations include flood-resilient infrastructure and inclusive early warning systems tailored for women in rural wards, offering a scalable tool for low-resource contexts.