Delineating flood susceptibility zones using novel ensemble models – An application of evidential belief function, relative frequency ratio, and Shannon entropy

Abstract

This paper contributes to developing novel ensemble models for delineating flood-prone areas in a West African context. One critical West African city with a history of flooding in Ghana, the Cape Coast Metropolis (CCM), was chosen with flood inventories, comprising 70% training and 30% validation, prepared as the basis for accurate prediction modeling. Furthermore, 13 conditioning parameters were chosen via multicollinearity evaluation. Three bivariate statistical algorithms, namely evidential belief function (EBF), relative frequency ratio (RFR), and Shannon entropy (SE) were combined through basic arithmetic operations to produce nine ensemble scenarios. Model performances were adjudged using the area under receiver operating characteristic curve (AUC/ROC) ratings and the overall best-performing model with a predictive accuracy of 99.6% was selected. Based on the findings, CCM’s total area was categorized into very low (20.0%), low (22.1%), moderate (20.2%), high (18.8%), and very high (18.9%) susceptibility zones. Moreover, the resultant map revealed middle portions down to the coast are most sensitive to floods compared to the northern part due to flat slope surfaces, decreasing vegetative cover, and low elevated lands. These delineated flood zones have substantial implications for national and local flood management to proactively plan and manage floods within the region and contribute to the global agenda of sustainable cities and communities.