Transforming flood risk prediction: An integrated approach using GIS, MCDM, AHP, and soil testing
Onuh, Joy
Onuh, Joy
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Abstract
Floods are among the most destructive natural disasters, yet many flood risk models lack accuracy, leaving communities unprepared. This study examines whether an integrated approach using GIS, MCDM, and AHP can improve flood risk prediction. In Makurdi, Nigeria, where floods frequently disrupt livelihoods, we analyzed eight critical flood-risk factors and validated their significance using a consistency ratio. Results showed that over 50% of the region falls within high-risk zones. A predictive regression model (R2 = 0.804) demonstrated strong accuracy, confirmed by laboratory soil analyses. This integrated approach offers scalable solutions for flood-prone regions worldwide.
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Graduate
Date
2025-06-02