Online LSTM Framework for Hurricane Trajectory Prediction (Papers Track)
Ding Wang (Michigan State University); Pang-Ning Tan (MSU)
Abstract
Hurricanes are high-intensity tropical cyclones that can cause severe damages when the storms make landfall. Accurate long-range prediction of hurricane trajectories is an important but challenging problem due to the complex interactions between the ocean and atmosphere systems. In this paper, we present a deep learning framework for hurricane trajectory forecasting by leveraging the outputs from an ensemble of dynamical (physical) models. The proposed framework employs a temporal decay memory unit for imputing missing values in the ensemble member outputs, coupled with an LSTM architecture for dynamic path prediction. The framework is extended to an online learning setting to capture concept drift present in the data. Empirical results suggest that the proposed framework significantly outperforms various baselines including the official forecasts from U.S. National Hurricane Center (NHC).