  Building on recent research for prediction of hurricane trajectories using
recurrent neural networks (RNNs), we have developed improved methods and
generalized the approach to predict Bayesian intervals in addition to simple
point estimates. Tropical storms are capable of causing severe damage, so
accurately predicting their trajectories can bring significant benefits to
cities and lives, especially as they grow more intense due to climate change
effects. By implementing the Bayesian interval using dropout in an RNN, we
improve the actionability of the predictions, for example by estimating the
areas to evacuate in the landfall region. We used an RNN to predict the
trajectory of the storms at 6-hour intervals. We used latitude, longitude,
windspeed, and pressure features from a Statistical Hurricane Intensity
Prediction Scheme (SHIPS) dataset of about 500 tropical storms in the Atlantic
Ocean. Our results show how neural network dropout values affect predictions
and intervals.
