Transforming flood risk prediction: An integrated approach using GIS, MCDM, AHP, and soil testing
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.
Primary Faculty Mentor Name
Linda Schadler
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Environmental Engineering
Primary Research Category
Engineering and Math Science
Transforming flood risk prediction: An integrated approach using GIS, MCDM, AHP, and soil testing
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.
