Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an example

Wanting Qin, Jun Tang, Cong Da Lu, Songyang Lao

Computational Geosciences · 05 March 2021

In one sentence. An LSTM with Kalman filter smoothing predicts Atlantic hurricane tracks from 1851–2016 historical data with the lowest error among compared LSTM variants.

Abstract

Abstract Trajectory data can objectively reflect the moving law of moving objects. Therefore, trajectory prediction has high application value. Hurricanes often cause incalculable losses of life and property, trajectory prediction can be an effective means to mitigate damage caused by hurricanes. With the popularization and wide application of artificial intelligence technology, from the perspective of machine learning, this paper trains a trajectory prediction model through historical trajectory data based on a long short-term memory (LSTM) network. An improved LSTM (ILSTM) trajectory prediction algorithm that improves the prediction of the simple LSTM is proposed, and the Kalman filter is used to filter the prediction results of the improved LSTM algorithm, which is called LSTM-KF. Through simulation experiments of Atlantic hurricane data from 1851 to 2016, compared to other LSTM and ILSTM algorithms, it is found that the LSTM-KF trajectory prediction algorithm has the lowest prediction error and the best prediction effect.

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