Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN
High-precision total electron content (TEC) prediction can improve the accuracy of the Global Navigation Satellite System (GNSS)-based applications. The existing deep learning models for TEC prediction mainly include long short-term memory (LSTM), convolutional long short-term memory (ConvLSTM), and...
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MDPI AG
2025-04-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/16/4/422 |
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| author | Haijun Liu Yan Ma Huijun Le Liangchao Li Rui Zhou Jian Xiao Weifeng Shan Zhongxiu Wu Yalan Li |
| author_facet | Haijun Liu Yan Ma Huijun Le Liangchao Li Rui Zhou Jian Xiao Weifeng Shan Zhongxiu Wu Yalan Li |
| author_sort | Haijun Liu |
| collection | DOAJ |
| description | High-precision total electron content (TEC) prediction can improve the accuracy of the Global Navigation Satellite System (GNSS)-based applications. The existing deep learning models for TEC prediction mainly include long short-term memory (LSTM), convolutional long short-term memory (ConvLSTM), and their variants, which contain only one temporal memory. These models may result in fuzzy prediction results due to neglecting spatial memory, as spatial memory is crucial for capturing the correlations of TEC within the TEC neighborhood. In this paper, we draw inspiration from the predictive recurrent neural network (PredRNN), which has dual memory states to construct a TEC prediction model named Multichannel ED-PredRNN. The highlights of our work include the following: (1) for the first time, a dual memory mechanism was utilized in TEC prediction, which can more fully capture the temporal and spatial features; (2) we modified the n vs. n structure of original PredRNN to an encoder–decoder structure, so as to handle the problem of unequal input and output lengths in TEC prediction; and (3) we expanded the feature channels by extending the Kp, Dst, and F10.7 to the same spatiotemporal resolution as global TEC maps, overlaying them together to form multichannel features, so as to fully utilize the influence of solar and geomagnetic activities on TEC. The proposed Multichannel ED-PredRNN was compared with COPG, ConvLSTM, and convolutional gated recurrent unit (ConvGRU) from multiple perspectives on a data set of 6 years, including comparisons at different solar activities, time periods, latitude regions, single stations, and geomagnetic storm periods. The results show that in almost all cases, the proposed Multichannel ED-PredRNN outperforms the three comparative models, indicating that it can more fully utilize temporal and spatial features to improve the accuracy of TEC prediction. |
| format | Article |
| id | doaj-art-78097842263142b68540266dcf7ffede |
| institution | OA Journals |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj-art-78097842263142b68540266dcf7ffede2025-08-20T02:28:27ZengMDPI AGAtmosphere2073-44332025-04-0116442210.3390/atmos16040422Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNNHaijun Liu0Yan Ma1Huijun Le2Liangchao Li3Rui Zhou4Jian Xiao5Weifeng Shan6Zhongxiu Wu7Yalan Li8Institute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, ChinaInstitute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, ChinaKey Laboratory of Earth and Planetary Physics, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100029, ChinaInstitute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, ChinaInstitute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, ChinaInstitute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, ChinaCollege of General Education, Hainan Vocational University, Haikou 570216, ChinaMicroelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, ChinaHigh-precision total electron content (TEC) prediction can improve the accuracy of the Global Navigation Satellite System (GNSS)-based applications. The existing deep learning models for TEC prediction mainly include long short-term memory (LSTM), convolutional long short-term memory (ConvLSTM), and their variants, which contain only one temporal memory. These models may result in fuzzy prediction results due to neglecting spatial memory, as spatial memory is crucial for capturing the correlations of TEC within the TEC neighborhood. In this paper, we draw inspiration from the predictive recurrent neural network (PredRNN), which has dual memory states to construct a TEC prediction model named Multichannel ED-PredRNN. The highlights of our work include the following: (1) for the first time, a dual memory mechanism was utilized in TEC prediction, which can more fully capture the temporal and spatial features; (2) we modified the n vs. n structure of original PredRNN to an encoder–decoder structure, so as to handle the problem of unequal input and output lengths in TEC prediction; and (3) we expanded the feature channels by extending the Kp, Dst, and F10.7 to the same spatiotemporal resolution as global TEC maps, overlaying them together to form multichannel features, so as to fully utilize the influence of solar and geomagnetic activities on TEC. The proposed Multichannel ED-PredRNN was compared with COPG, ConvLSTM, and convolutional gated recurrent unit (ConvGRU) from multiple perspectives on a data set of 6 years, including comparisons at different solar activities, time periods, latitude regions, single stations, and geomagnetic storm periods. The results show that in almost all cases, the proposed Multichannel ED-PredRNN outperforms the three comparative models, indicating that it can more fully utilize temporal and spatial features to improve the accuracy of TEC prediction.https://www.mdpi.com/2073-4433/16/4/422total electron content (TEC)multichannel ED-PredRNNspatiotemporal predictionsolar activitymagnetic storms |
| spellingShingle | Haijun Liu Yan Ma Huijun Le Liangchao Li Rui Zhou Jian Xiao Weifeng Shan Zhongxiu Wu Yalan Li Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN Atmosphere total electron content (TEC) multichannel ED-PredRNN spatiotemporal prediction solar activity magnetic storms |
| title | Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN |
| title_full | Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN |
| title_fullStr | Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN |
| title_full_unstemmed | Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN |
| title_short | Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN |
| title_sort | global ionospheric tec map prediction based on multichannel ed predrnn |
| topic | total electron content (TEC) multichannel ED-PredRNN spatiotemporal prediction solar activity magnetic storms |
| url | https://www.mdpi.com/2073-4433/16/4/422 |
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