Title: Predicting Hurricane Trajectories using a Recurrent Neural Network,
Abstract: Hurricanes are cyclones circulating about a defined center whose closed wind
speeds exceed 75 mph originating over tropical and subtropical waters. At
landfall, hurricanes can result in severe disasters. The accuracy of predicting
their trajectory paths is critical to reduce economic loss and save human
lives. Given the complexity and nonlinearity of weather data, a recurrent
neural network (RNN) could be beneficial in modeling hurricane behavior. We
propose the application of a fully connected RNN to predict the trajectory of
hurricanes. We employed the RNN over a fine grid to reduce typical truncation
errors. We utilized their latitude, longitude, wind speed, and pressure
publicly provided by the National Hurricane Center (NHC) to predict the
trajectory of a hurricane at 6-hour intervals. Results show that this proposed
technique is competitive to methods currently employed by the NHC and can
predict up to approximately 120 hours of hurricane path.