This presentation will discuss a newly proposed Hurricane Impact Level Ranking System and the artificial neural network model developed for its forecasting use. The Hurricane Impact Level Ranking System uses thresholds of economic damage to categorize historical events in order to provide a comparative level for a new oncoming hurricane event within the United States. This approach proposes a new way to synthesize these multiple hazards from one hurricane event into a simpler form the public more easily comprehends: cost. In real time use, an artificial neural network relies on established patterns from historical events as a comparative algorithm to forecast results for a new event. The results of the research conducted in building this model have led to additional understanding of how best to approach a multivariable assessment of a natural hazard. Use of this framework and ranking system could improve how the scientific community conveys risk and vulnerability to the public during natural hazard events in order to minimize cases of crying wolf and subsequent disregard to later warnings.