Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement
The F10.7 index is crucial for assessing solar activity, significantly impacting communication, navigation, and satellite operations. The intrinsic complexity and variability of solar activity often result in sudden perturbations in the F10.7 index, compromising the accuracy and stability of forecas...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/ad90a2 |
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| author | Shuainan Yan Yanmei Cui Bingxian Luo Liqin Shi Yanxia Cai |
| author_facet | Shuainan Yan Yanmei Cui Bingxian Luo Liqin Shi Yanxia Cai |
| author_sort | Shuainan Yan |
| collection | DOAJ |
| description | The F10.7 index is crucial for assessing solar activity, significantly impacting communication, navigation, and satellite operations. The intrinsic complexity and variability of solar activity often result in sudden perturbations in the F10.7 index, compromising the accuracy and stability of forecasts. To address this challenge, we propose a novel prediction strategy that separately forecasts fundamental trends driven by the medium-to-long-term evolution of the solar cycle and the 27 day rotational modulation, along with transient disturbances caused by solar flares and the rapid evolution of active regions. These forecasts are then integrated to enhance overall prediction accuracy. We incorporate additional features such as the soft X-ray flare index (FI _SXR ), magnetic type of the active region (new_Mag), and X-ray background flux (XBF) to enhance the understanding of the underlying physical processes of solar activity. Our experiments, conducted using advanced forecasting models on the SG-F10.7-All data set, validate the efficacy of our proposed strategy. Notably, the iTransformer model demonstrates superior performance in both short-term and medium-term forecasting scenarios. The inclusion of FI _SXR , new_Mag, and XBF significantly improves forecasting accuracy, highlighting their importance in improving the F10.7 index predictions. Our method outperforms international models from the Space Weather Prediction Center, British Geological Survey, and Collecte Localisation Satellites, exhibiting greater accuracy and adaptability across various solar activity phases. This finding provides a novel approach for precise forecasting of the F10.7 index. |
| format | Article |
| id | doaj-art-8439ee8b12d2450c99e4f8720efa083f |
| institution | Kabale University |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-8439ee8b12d2450c99e4f8720efa083f2025-01-09T06:48:47ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127612810.3847/1538-4365/ad90a2Research on F10.7 Index Prediction Based on Factor Decomposition and Feature EnhancementShuainan Yan0https://orcid.org/0000-0001-7460-3275Yanmei Cui1https://orcid.org/0000-0001-8037-777XBingxian Luo2https://orcid.org/0000-0002-7762-2786Liqin Shi3Yanxia Cai4State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People's Republic of China ; caiyx@nssc.ac.cn; University of Chinese Academy of Sciences , Beijing 100190, People's Republic of ChinaState Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People's Republic of China ; caiyx@nssc.ac.cnState Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People's Republic of China ; caiyx@nssc.ac.cn; University of Chinese Academy of Sciences , Beijing 100190, People's Republic of ChinaState Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People's Republic of China ; caiyx@nssc.ac.cn; University of Chinese Academy of Sciences , Beijing 100190, People's Republic of ChinaState Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences , Beijing 100190, People's Republic of China ; caiyx@nssc.ac.cnThe F10.7 index is crucial for assessing solar activity, significantly impacting communication, navigation, and satellite operations. The intrinsic complexity and variability of solar activity often result in sudden perturbations in the F10.7 index, compromising the accuracy and stability of forecasts. To address this challenge, we propose a novel prediction strategy that separately forecasts fundamental trends driven by the medium-to-long-term evolution of the solar cycle and the 27 day rotational modulation, along with transient disturbances caused by solar flares and the rapid evolution of active regions. These forecasts are then integrated to enhance overall prediction accuracy. We incorporate additional features such as the soft X-ray flare index (FI _SXR ), magnetic type of the active region (new_Mag), and X-ray background flux (XBF) to enhance the understanding of the underlying physical processes of solar activity. Our experiments, conducted using advanced forecasting models on the SG-F10.7-All data set, validate the efficacy of our proposed strategy. Notably, the iTransformer model demonstrates superior performance in both short-term and medium-term forecasting scenarios. The inclusion of FI _SXR , new_Mag, and XBF significantly improves forecasting accuracy, highlighting their importance in improving the F10.7 index predictions. Our method outperforms international models from the Space Weather Prediction Center, British Geological Survey, and Collecte Localisation Satellites, exhibiting greater accuracy and adaptability across various solar activity phases. This finding provides a novel approach for precise forecasting of the F10.7 index.https://doi.org/10.3847/1538-4365/ad90a2Solar radio emission |
| spellingShingle | Shuainan Yan Yanmei Cui Bingxian Luo Liqin Shi Yanxia Cai Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement The Astrophysical Journal Supplement Series Solar radio emission |
| title | Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement |
| title_full | Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement |
| title_fullStr | Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement |
| title_full_unstemmed | Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement |
| title_short | Research on F10.7 Index Prediction Based on Factor Decomposition and Feature Enhancement |
| title_sort | research on f10 7 index prediction based on factor decomposition and feature enhancement |
| topic | Solar radio emission |
| url | https://doi.org/10.3847/1538-4365/ad90a2 |
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