Data-Based Models for Hurricane Evolution Prediction: A Deep Learning Approach
Rikhi Bose, Adam L. Pintar, Emil Simiu
Fast and accurate prediction of hurricane evolution from genesis onwards is needed to reduce loss of life and enhance community resilience. In this work, a novel model development methodology for predicting storm trajectory is proposed based on two classes of Recurrent Neural Networks (RNN). The RNN models are trained on input features available in or derived from the HURDAT2 North Atlantic hurricane database maintained by the National Hurricane Center (NHC). The models use probabilities of storms passing through any location, computed from historical data. A de- tailed analysis of model forecasting error shows that Many-To-One prediction models are less accurate than Many-To-Many models owing to compounded error accumulation, with the exception of 6 − hr predictions, for which the two types of model perform comparably. Application to 75 or more test storms in the North Atlantic basin showed that, for short-term forecasting up to 12 hours, the Many-to-Many RNN storm trajectory prediction models presented herein are significantly faster than ensemble models used by the NHC, while entailing errors of comparable magnitude.
Thirty-Sixth AAAI Conference on Artificial Intelligence 2022
February 22-March 1, 2022
Time series forecasting, Long Short-Term Memory (LSTM), North Atlantic hurricanes, Recurrent Neural Networks (RNN), HURDAT2
, Pintar, A.
and Simiu, E.
Data-Based Models for Hurricane Evolution Prediction: A Deep Learning Approach, Thirty-Sixth AAAI Conference on Artificial Intelligence 2022 , Vancouver, CA
(Accessed December 7, 2022)