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Open AccessArticle

The Comparison of Predicting Storm-time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq

1
Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China
2
Key Laboratory of Space Weather, National Center for Space Weather, China Meteorological Administration, Beijing 100081, China
3
School of Electronic Information, Wuhan University, Wuhan 430072, China
4
Jiangxi Institute of Computing Technology, Nanchang 330003, China
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(4), 316; https://doi.org/10.3390/atmos11040316
Received: 20 February 2020 / Revised: 22 March 2020 / Accepted: 23 March 2020 / Published: 25 March 2020
Ionospheric structure usually changes dramatically during a strong geomagnetic storm period, which will significantly affect the short-wave communication and satellite navigation systems. It is critically important to make accurate ionospheric predictions under the extreme space weather conditions. However, ionospheric prediction is always a challenge, and pure physical methods often fail to get a satisfactory result since the ionospheric behavior varies greatly with different geomagnetic storms. In this paper, in order to find an effective prediction method, one traditional mathematical method (autoregressive integrated moving average—ARIMA) and two deep learning algorithms (long short-term memory—LSTM and sequence-to-sequence—Seq2Seq) are investigated for the short-term predictions of ionospheric TEC (Total Electron Content) under different geomagnetic storm conditions based on the MIT (Massachusetts Institute of Technology) madrigal observation from 2001 to 2016. Under the extreme condition, the performance limitation of these methods can be found. When the storm is stronger, the effective prediction horizon of the methods will be shorter. The statistical analysis shows that the LSTM can achieve the best prediction accuracy and is robust for the accurate trend prediction of the strong geomagnetic storms. In contrast, ARIMA and Seq2Seq have relatively poor performance for the prediction of the strong geomagnetic storms. This study brings new insights to the deep learning applications in the space weather forecast.
Keywords: ionospheric TEC; deep learning; geomagnetic storm; time series prediction; space weather ionospheric TEC; deep learning; geomagnetic storm; time series prediction; space weather
MDPI and ACS Style

Tang, R.; Zeng, F.; Chen, Z.; Wang, J.-S.; Huang, C.-M.; Wu, Z. The Comparison of Predicting Storm-time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq. Atmosphere 2020, 11, 316.

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