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Abstract
Flooding brought on by excessive rainfall is one of the frequently occurring and widely reported disasters affecting human existence. The purpose of this study is to create flood risk maps of Ikpoba Okha which can be used for predicting the level of vulnerability due to rapid urban development taking place in recent times. The procedure to achieve this involved using the method of Analytical Hierarchy Process (AHP) in a Geographic Information System (GIS) environment. Among the fundamental datasets requirements for the project were: cloud-free high-resolution satellite images, SRTM DEM data, FAO soil data, rainfall data, etc. Maps of flood-enhancing elements, such as
flood risk vulnerability mapping, were created in Geographic Information Systems using the same scale of 1: 200,000 and geographic coordinate system (WGS 1984 UTM zone 31N). This multiparametric technique includes rainfall distribution, elevation and slope, drainage network and density, land use/land cover, and soil type, among other flood determinants. All the output raster maps
were first ranked using the "Weighted Linear Combination" method with a grid cell size of 0.0028 mm before being sent for Multi-Criteria Analysis (MCA). The computation of the consistency ratio at an acceptable level of 0.055 further confirmed the model's validity. Additionally, the research found topography and rainfall as the most significant factors contributing to floods in Benin City.
flood risk vulnerability mapping, were created in Geographic Information Systems using the same scale of 1: 200,000 and geographic coordinate system (WGS 1984 UTM zone 31N). This multiparametric technique includes rainfall distribution, elevation and slope, drainage network and density, land use/land cover, and soil type, among other flood determinants. All the output raster maps
were first ranked using the "Weighted Linear Combination" method with a grid cell size of 0.0028 mm before being sent for Multi-Criteria Analysis (MCA). The computation of the consistency ratio at an acceptable level of 0.055 further confirmed the model's validity. Additionally, the research found topography and rainfall as the most significant factors contributing to floods in Benin City.
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