Prediction of Bayesian Intervals for Tropical Storms
This work addresses the need for more actionable storm predictions to aid cities and lives, but it is incremental as it builds on existing RNN methods for hurricane trajectory prediction.
The paper tackles the problem of predicting tropical storm trajectories by extending recurrent neural networks to output Bayesian intervals, not just point estimates, using dropout to improve actionability for evacuation planning. Results demonstrate how dropout values affect predictions and intervals, though no concrete numerical improvements are provided.
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.