Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM
Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC tempora...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/12/3064 |
| _version_ | 1797592782530936832 |
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| author | Liangchao Li Haijun Liu Huijun Le Jing Yuan Weifeng Shan Ying Han Guoming Yuan Chunjie Cui Junling Wang |
| author_facet | Liangchao Li Haijun Liu Huijun Le Jing Yuan Weifeng Shan Ying Han Guoming Yuan Chunjie Cui Junling Wang |
| author_sort | Liangchao Li |
| collection | DOAJ |
| description | Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model. |
| first_indexed | 2024-03-11T01:59:01Z |
| format | Article |
| id | doaj.art-8a8888bd35894565a1e97f29fc28d4ee |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T01:59:01Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8a8888bd35894565a1e97f29fc28d4ee2023-11-18T12:25:53ZengMDPI AGRemote Sensing2072-42922023-06-011512306410.3390/rs15123064Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTMLiangchao Li0Haijun Liu1Huijun Le2Jing Yuan3Weifeng Shan4Ying Han5Guoming Yuan6Chunjie Cui7Junling Wang8Institute 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, Institute of Disaster Prevention, Langfang 065201, ChinaInstitute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, ChinaSchool of Information Engineering, Institute of Disaster Prevention, Langfang 065201, ChinaInstitute of Intelligent Emergency Information Processing, Institute of Disaster Prevention, Langfang 065201, ChinaBeijing Jingwei Textile Machinery New Technology Co., Ltd., Beijing 100176, ChinaCollege of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, ChinaTotal electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model.https://www.mdpi.com/2072-4292/15/12/3064ionospheric TECConvLSTMencoder–decoderspatiotemporalgeomagnetic storm |
| spellingShingle | Liangchao Li Haijun Liu Huijun Le Jing Yuan Weifeng Shan Ying Han Guoming Yuan Chunjie Cui Junling Wang Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM Remote Sensing ionospheric TEC ConvLSTM encoder–decoder spatiotemporal geomagnetic storm |
| title | Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM |
| title_full | Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM |
| title_fullStr | Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM |
| title_full_unstemmed | Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM |
| title_short | Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM |
| title_sort | spatiotemporal prediction of ionospheric total electron content based on ed convlstm |
| topic | ionospheric TEC ConvLSTM encoder–decoder spatiotemporal geomagnetic storm |
| url | https://www.mdpi.com/2072-4292/15/12/3064 |
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