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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Acknowledgements
This work was supported by the National Natural Science Foundation of China (62073330), Natural Science Foundation of Hunan Province (2019JJ20021), NUDT Scientific Research Project (ZK18-02-12), Huxiang Young Talents (2018RS3079) and Innovation project for graduate students in Hunan Province (CX20190046).
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Qin, W., Tang, J., Lu, C. et al. Trajectory prediction based on long short-term memory network and Kalman filter using hurricanes as an example. Comput Geosci 25, 1005–1023 (2021). https://doi.org/10.1007/s10596-021-10037-2
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DOI: https://doi.org/10.1007/s10596-021-10037-2