Using Carrying Capacity and Multi-Criteria Analysis to Proactively Manage Floods in the Caribbean

  • Baban, S. M. J.,1 and Aliasgar, K.,2 .


Flooding is the most common hazard that affects Caribbean territories, Trinidad included, leading to economic losses and death. This paper promotes a Geoinformatics founded methodology for mapping areas that have the potential to flood ‘flood prone areas’ using Binary Logistic Regression to identify the carrying capacity of watersheds for flooding. The geophysical terrain characteristics such as slope, elevation, geology and rainfall for these susceptible areas for flooding were used. Binary Logistic Regression was used in determining the significant characteristics in predicting the flooded watersheds in comparison to the non flooded watersheds. It was found that the characteristics of the watersheds by themselves were not significant in predicting floods, but when interaction between these characteristics were considered it was possible to predict floods at approximately 83% accuracy. The developed model is useful for proactively managing floods, identifying flood prone watersheds, establishing flood insurance premium rates, and identifying areas having unique, natural and beneficial functions.