Abstract
Catastrophes have caused tremendous damages in human history and triggered record high post-disaster relief from the governments. The research of catastrophic modeling can help estimate the effects of natural disasters like hurricanes, floods, surges, and earthquakes. In every Atlantic hurricane season, the state of Florida in the United States has the potential to suffer economic and human losses from hurricanes. The Florida Public Hurricane Loss Model (FPHLM), funded by the Florida Office of Insurance Regulation, has assisted Florida and the residential insurance industry for more than a decade. How to process big data for historical hurricanes and insurance companies remains a challenging research topic for cat models. In this paper, the FPHLM's novel integrated domain knowledge assisted big data processing system is introduced and its effectiveness of data processing error prevention is presented.
