A Sparse Recurrent Neural Network for Trajectory Prediction of Atlantic Hurricanes

Hurricanes constitute major natural disasters that lead to destruction and loss of lives. Therefore, to reduce economic loss and to save human lives, an accurate forecast of hurricane occurrences is crucial. Despite the availability of data and advanced forecasting techniques, there is a need for effective methods with higher accuracy of prediction. We propose a sparse Recurrent Neural Network (RNN) with flexible topology for trajectory prediction of the Atlantic hurricanes. Topology of the RNN along with the strength of the connections are evolved by a customized Genetic Algorithm. The network is particularly suitable for modeling of hurricanes which have complex systems with unknown dynamics. For prediction of the future trajectories of a target hurricane, the Dynamic Time Warping (DTW) distances between direction of the target hurricane over time, and other hurricanes in the dataset are determined and compared. The most similar hurricanes to the target hurricane are then used for training of the network. Comparisons between the actual tracks of the hurricanes DEAN, SANDY, ISSAC and HUMBERTO, and the generated predictions by the sparse RNN for one and two steps ahead of time show that our approach is quite promising for this aim.