Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China
Abstract An increasing number of terrestrial‐ and space‐based radio‐communication systems are influenced by the ionospheric space weather, making the ionospheric state increasingly important to forecast. In this study, a novel extended encoder‐decoder long short‐term memory extended (ED‐LSTME) neura...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-04-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2020SW002706 |
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| _version_ | 1841536326409650176 |
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| author | Pan Xiong Dulin Zhai Cheng Long Huiyu Zhou Xuemin Zhang Xuhui Shen |
| author_facet | Pan Xiong Dulin Zhai Cheng Long Huiyu Zhou Xuemin Zhang Xuhui Shen |
| author_sort | Pan Xiong |
| collection | DOAJ |
| description | Abstract An increasing number of terrestrial‐ and space‐based radio‐communication systems are influenced by the ionospheric space weather, making the ionospheric state increasingly important to forecast. In this study, a novel extended encoder‐decoder long short‐term memory extended (ED‐LSTME) neural network, which can predict ionospheric total electron content (TEC) is proposed. Useful inherent features were automatically extracted from the historical TEC by LSTM layers, and the performance of the proposed model was enhanced by considering solar flux and geomagnetic activity data. The proposed ED‐LSTME model was validated using 15‐min TEC values from GPS measurements over one solar cycle (from January 2006 to July 2018) collected at 15 GPS stations in China. Different assessment experiments were conducted in different geographical locations and seasons as well as under varying geomagnetic activities, to comprehensively evaluate the model's performance. These comparative experiments were conducted using an ED‐LSTM, a traditional LSTM, a deep neural network, autoregressive integrated moving average, and the 2016 International Reference Ionosphere models. The results indicated that the ED‐LSTME model is superior to the other statistical models, with R2 and root mean square error values of 0.89 and 12.09 TECU, respectively. In addition, TEC was adequately predicted under different ionospheric conditions, and satisfactory results were obtained even under geomagnetically disturbed conditions. These results suggest that the prediction performance could be significantly improved by utilizing auxiliary data. These observations confirm that the proposed model outperforms several state‐of‐the‐art models in making predictions at different times and under diverse conditions. |
| format | Article |
| id | doaj-art-4619fbc827f247ffac86023d59343d34 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-4619fbc827f247ffac86023d59343d342025-01-14T16:31:28ZengWileySpace Weather1542-73902021-04-01194n/an/a10.1029/2020SW002706Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over ChinaPan Xiong0Dulin Zhai1Cheng Long2Huiyu Zhou3Xuemin Zhang4Xuhui Shen5Institute of Earthquake Forecasting China Earthquake Administration Beijing ChinaKey Laboratory of Earthquake Geodesy Institute of Seismology China Earthquake Administration Wuhan ChinaSchool of Computer Science and Engineering Nanyang Technological University Singapore SingaporeSchool of Informatics University of Leicester Leicester UKInstitute of Earthquake Forecasting China Earthquake Administration Beijing ChinaNational Institute of Natural Hazards Ministry of Emergency Management of China Beijing ChinaAbstract An increasing number of terrestrial‐ and space‐based radio‐communication systems are influenced by the ionospheric space weather, making the ionospheric state increasingly important to forecast. In this study, a novel extended encoder‐decoder long short‐term memory extended (ED‐LSTME) neural network, which can predict ionospheric total electron content (TEC) is proposed. Useful inherent features were automatically extracted from the historical TEC by LSTM layers, and the performance of the proposed model was enhanced by considering solar flux and geomagnetic activity data. The proposed ED‐LSTME model was validated using 15‐min TEC values from GPS measurements over one solar cycle (from January 2006 to July 2018) collected at 15 GPS stations in China. Different assessment experiments were conducted in different geographical locations and seasons as well as under varying geomagnetic activities, to comprehensively evaluate the model's performance. These comparative experiments were conducted using an ED‐LSTM, a traditional LSTM, a deep neural network, autoregressive integrated moving average, and the 2016 International Reference Ionosphere models. The results indicated that the ED‐LSTME model is superior to the other statistical models, with R2 and root mean square error values of 0.89 and 12.09 TECU, respectively. In addition, TEC was adequately predicted under different ionospheric conditions, and satisfactory results were obtained even under geomagnetically disturbed conditions. These results suggest that the prediction performance could be significantly improved by utilizing auxiliary data. These observations confirm that the proposed model outperforms several state‐of‐the‐art models in making predictions at different times and under diverse conditions.https://doi.org/10.1029/2020SW002706GPS‐TEC modelingGPS‐TEC predictionionospherelong short‐term memory neural network |
| spellingShingle | Pan Xiong Dulin Zhai Cheng Long Huiyu Zhou Xuemin Zhang Xuhui Shen Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China Space Weather GPS‐TEC modeling GPS‐TEC prediction ionosphere long short‐term memory neural network |
| title | Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China |
| title_full | Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China |
| title_fullStr | Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China |
| title_full_unstemmed | Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China |
| title_short | Long Short‐Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China |
| title_sort | long short term memory neural network for ionospheric total electron content forecasting over china |
| topic | GPS‐TEC modeling GPS‐TEC prediction ionosphere long short‐term memory neural network |
| url | https://doi.org/10.1029/2020SW002706 |
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