ED-SA-ConvLSTM: A Novel Spatiotemporal Prediction Model and Its Application in Ionospheric TEC Prediction

Yalan Li, Haiming Deng (), Jian Xiao, Bin Li, Tao Han, Jianquan Huang and Haijun Liu
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Yalan Li: Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, China
Haiming Deng: Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, China
Jian Xiao: Institute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, China
Bin Li: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Tao Han: Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, China
Jianquan Huang: Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, China
Haijun Liu: Institute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, China

Mathematics, 2025, vol. 13, issue 12, 1-18

Abstract: The ionospheric total electron content (TEC) has complex spatiotemporal variations, making its spatiotemporal prediction challenging. Capturing long-range spatial dependencies is of great significance for improving the spatiotemporal prediction accuracy of TEC. Existing work based on Convolutional Long Short-Term Memory (ConvLSTM) primarily relies on convolutional operations for spatial feature extraction, which are effective at capturing local spatial correlations, but struggle to model long-range dependencies, limiting their predictive performance. Self-Attention Convolutional Long Short-Term Memory (SA-ConvLSTM) can selectively store and focus on long-range spatial dependencies, but it requires the input length and output length to be the same due to its “n vs. n” structure, limiting its application. To solve this problem, this paper proposes an encoder-decoder SA-ConvLSTM, abbreviated as ED-SA-ConvLSTM. It can effectively capture long-range spatial dependencies using SA-ConvLSTM and achieve unequal input-output lengths through encoder–decoder structure. To verify its performance, the proposed ED-SA-ConvLSTM was compared with C1PG, ConvLSTM, and PredRNN from multiple perspectives in the area of 12.5° S–87.5° N, 25° E–180° E, including overall quantitative comparison, comparison across different months, comparison at different latitude regions, visual comparisons, and comparison under extreme situations. The results have shown that, in the vast majority of cases, the proposed ED-SA-ConvLSTM outperforms the comparative models.

Keywords: ionospheric total electron content (TEC); long-range spatial dependencies; self-attention; spatiotemporal prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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