LAUSR

Flood is a natural hazard affecting human life and ecosystem globally causing catastrophic disasters. Most flood-induced socioeconomic losses are exacerbated by unabated urban development, population upsurge, unregulated municipal systems, and indiscriminate land use. Therefore, implementation of a flood prediction system can potentially help mitigate flood-induced consequences. In this study, a framework of multi-criteria analysis incorporating geographic information system, fuzzy analytic hierarchy process, and bivariate statistics-based methods was developed for flood hazard index determination. Flood-prone areas were identified based on six factors (hydrological aspects and land cover): elevation, aspect, slope, flow accumulation, rainfall, and land cover map. To generate a flood hazard index, each one of the factors was classified into five categories: very low, low, moderate, high, and very high; the factors were then combined and processed using the proposed methodology. Obtained overall maps have been adjusted with socioeconomic data such as gross domestic product to relate the flood exposure to economic and demographic factors in Mombasa County, Kenya. Results suggest that the County is largely dominated by areas with a high flood hazard index due to its location and shoreline. Fort Jesus, the UNESCO World Heritage site, is currently under high risk of flood as shown by the flood hazard index, while most of the shoreline is at very high risk of flooding.