Predicting Hurricane Trajectories Using a Recurrent Neural Network

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Presented by

Yishu Duan, Xibei Di, Xin Yan


Hurricanes originate in the warm water of the Caribbean Sea and Atlantic Ocean, and generally travel from their origin to the north, northwest, or northeast. Hurricanes are usually accompanied by strong winds, heavy rainfall, and dangerous tides, as one of the most common natural disasters on the planet, hurricanes could threaten the safety of people’s economic property assets and human lives. This makes predicting the hurricane paths by modeling the hurricane behavior extremely essential.

Recurrent Neural Networks (RNNs) are a kind of artificial neural networks, where the weights of it can be modified to make the model learn complex dynamic time-dependent behavior. A RNN can effectively simulate the complex nonlinear temporal relationship of hurricanes, which can improve the future prediction of the accuracy of hurricane path.

Thus, in this paper, fully connected recurrent neural networks using a grid model are built for hurricane track prediction, and the result will be compared with other hurricane predicting techniques.

Related Work

Model Architecture