GIS-based multi-criteria analytical hierarchy process modelling for urban flood vulnerability analysis, Accra Metropolis

Raymond Seyeram Nkonu (), Mary Antwi, Mark Amo-Boateng and Benjamin Wullobayi Dekongmen
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Raymond Seyeram Nkonu: University of Energy and Natural Resources
Mary Antwi: University of Energy and Natural Resources
Mark Amo-Boateng: University of Energy and Natural Resources
Benjamin Wullobayi Dekongmen: University of Energy and Natural Resources

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 117, issue 2, No 15, 1568 pages

Abstract: Abstract The Accra Metropolis, the capital of Ghana, has been hit by multiple devastating floods in recent decades, resulting in severe social, economic, and environmental implications. The present study aimed to develop a GIS-based model for analysing and evaluating flood vulnerability in the Accra Metropolitan Area. The framework centres on the multi-criteria analytical hierarchy process within the GIS platform. For this purpose, the following flood-contributing parameters were considered for the modelling: LULC, elevation, slope, soil, and drainage density. The AHP approach was used to compute the influences of all parameters for a comprehensive weighted-overlay analysis in the flood vulnerability assessment. Thus, composite urban flood vulnerability indices to highlight flood-susceptible zones for the years 2007, 2010, 2015, and 2020 were generated. The resulting maps revealed that areas with high flood vulnerability increased between 2007 and 2020. The significant majority of flood occurrences (approximately 60%) that occurred throughout the research period were within flood-prone zones. These zones are largely located in the south-central and south-western regions of Metropolis, where the combination of low-lying terrain, extensive impervious surfaces, and intense urbanization generate favourable conditions for flooding. The computed receiver operating characteristic (ROC) curve resulted in an area under the curve (AUC) value of 0.916, indicating a strong correlation between the analysed flood-prone areas and the ground truth data. The methodology and evidence-based results presented in the study will assist decision-makers in formulating medium- to long-term mitigation measures to reduce flood-related damages and employ proper future land-use planning.

Keywords: Urban hydrology; Inundation; Land use/land cover; Remote sensing; Flood susceptibility mapping (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-05915-0

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