4.2 Article

Integrating multi-criteria analysis and geospatial applications for mapping flood hazards in Sekondi-Takoradi Metropolis, Ghana

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JOURNAL OF AFRICAN EARTH SCIENCES
卷 209, 期 -, 页码 -

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PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jafrearsci.2023.105102

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Susceptibility; Urban; Remote sensing; Geographic information system; Analytical hierarchy process (AHP); Africa

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This study conducted a spatial assessment and mapping of flood hazards in the Sekondi-Takoradi Metropolis (STM) of Ghana using the Analytical Hierarchy Process (AHP) and geographical information system. The results indicate that about 12% of STM lands are in very high flood zones and 24% are in high flood zones. The lower portions of STM are identified as the most susceptible to inundation. These findings provide valuable insights for effective flood planning and prevention in STM.
Despite numerous structural and non-structural measures to curb the problem, flood disasters have become a recurring theme globally. Reducing vulnerability to flooding requires a constant reassessment of flood hazard areas since their spatial extent keeps changing. This paper sought to map flood hazards in the Sekondi-Takoradi Metropolis (STM) of Ghana. STM experiences frequent flooding incidents, rapid urbanization, and massive encroachment of lands, partly due to a booming petroleum industry. However, an empirical spatial assessment of its flood hotspots is non-existent. We applied a multi-criteria analysis (specifically the Analytical Hierarchy Process [AHP]) to address this issue by processing causative flood parameters derived from remotely sensed images using a geographical information system. The principal results show that about 12% and 24% of STM lands are in very high and high flood zones, respectively. The rest of the land is in moderate (24.6%), low (24.3%), and very low (15%) flood zones. Areas along the lower portions of STM are identified as most susceptible to inundation. A computed consistency ratio of 0.03 is indicative of the efficiency of the study's model. These findings provide relevant stakeholders with an extensive understanding of the different flood zones for effective flood planning and prevention in STM.

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