Modeling the economic impact of incoming tropical cyclones using machine learning

Hurricanes and tropical storms are natural hazards with the capacity to devastate coastal regions. Understanding this catastrophic potential is critical to support informed decision-making at local, state, and federal levels. The primary objective of this research is to use machine learning to rigorously explore and quantify the relationship of tropical cyclone characteristics to their destructive outcomes on the coast of the USA. Historical data on hurricanes and tropical storms are identified and curated to support supervised learning. A novel storm damage ratio is introduced to address the inherent challenge of comparing damage to regions with distinct assets and population. Multiple mathematical models to predict economic impacts from tropical events are created using machine learning methods, and the results are compared. Finally, the storm features that most influence the accuracy of predictions are identified and ranked.