Hurricanes present major challenges in the U.S. due to their devastating impacts. Mitigating these risks is important, and the insurance industry is central in this effort, using intricate statistical models for risk assessment. However, these models often neglect key temporal and spatial hurricane patterns and are limited by data scarcity. This study introduces a refined approach combining the ARIMA model and K-MEANS to better capture hurricane trends, and an Autoencoder for enhanced hurricane simulations. Our experiments show that this hybrid methodology effectively simulate historical hurricane behaviors while providing detailed projections of potential future trajectories and intensities. Moreover, by leveraging a comprehensive yet selective dataset, our simulations enrich the current understanding of hurricane patterns and offer actionable insights for risk management strategies.

Artificial intelligence and machine learning have never been more prominent in the public forum. CBS's 60 Minutes featured earlier this year a segment promising myriad benefits to humanity in fields ranging from medicine to manufacturing. World chess champion Garry Kasparov recently debuted a book on his historic chess game with IBM's Deep Blue. Industry luminaries continue to opine about the potential threat by AI to human jobs and even humanity itself. Much of the conversation focuses on machines replacing humans.