Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM
文献类型：期刊论文
| 作者 | Li, Liangchao5; Liu, Haijun5; Le, Huijun1; Yuan, Jing4; Shan, Weifeng5; Han, Ying4; Yuan, Guoming5; Cui, Chunjie3; Wang, Junling2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-06-01 |
| 卷号 | 15期号:12页码:21 |
| 关键词 | ionospheric TEC ConvLSTM encoder-decoder spatiotemporal geomagnetic storm |
| DOI | 10.3390/rs15123064 |
| 英文摘要 | 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 & DEG;-45 & DEG;N, 90 & DEG;-130 & DEG;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. |
| WOS关键词 | INTERNATIONAL REFERENCE IONOSPHERE ; TIME-SERIES ; TEC ; MODEL ; PERFORMANCE ; ALGORITHM ; ARMA ; LONG ; MAPS ; GPS |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001016240200001 |
| 源URL | [http://ir.iggcas.ac.cn/handle/132A11/111236]  |
| 专题 | 地质与地球物理研究所_中国科学院地球与行星物理重点实验室 |
| 通讯作者 | Liu, Haijun |
| 作者单位 | 1.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing 100029, Peoples R China 2.Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Peoples R China 3.Beijing Jingwei Text Machinery New Technol Co Ltd, Beijing 100176, Peoples R China 4.Inst Disaster Prevent, Sch Informat Engn, Langfang 065201, Peoples R China 5.Inst Intelligent Emergency Informat Proc, Inst Disaster Prevent, Langfang 065201, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Liangchao,Liu, Haijun,Le, Huijun,et al. Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM[J]. REMOTE SENSING,2023,15(12):21. |
| APA | Li, Liangchao.,Liu, Haijun.,Le, Huijun.,Yuan, Jing.,Shan, Weifeng.,...&Wang, Junling.(2023).Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM.REMOTE SENSING,15(12),21. |
| MLA | Li, Liangchao,et al."Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM".REMOTE SENSING 15.12(2023):21. |
入库方式： OAI收割
来源：地质与地球物理研究所
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