A real-time solar flare forecasting system with deep learning methods
文献类型：期刊论文
| 作者 | Yan, Pengchao6; Li, Xuebao6; Zheng, Yanfang6; Dong L(董亮)2,5 ; Yan, Shuainan3,4; Zhang, Shunhuang6; Ye, Hongwei6; Li, Xuefeng6; Lü, Yongshang6; Ling, Yi6
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| 刊名 | ASTROPHYSICS AND SPACE SCIENCE
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| 出版日期 | 2024-10 |
| 卷号 | 369期号:10 |
| 关键词 | The sun (1693) Solar flares (1496) Astronomy image processing (2306) Convolutional neural networks (1938) |
| ISSN号 | 0004-640X |
| DOI | 10.1007/s10509-024-04374-8 |
| 产权排序 | 第2完成单位 |
| 文献子类 | Article |
| 英文摘要 | In this study, we develop five deep learning models, a Convolutional Neural Network (CNN) model, a CNN model with Squeeze-and-Excitation Attention(CNN-SE), a CNN model with Convolutional Block Attention Module (CNN-CBAM), a CNN model with Efficient Channel Attention (CNN-ECA), and a Vision Transformer (ViT) model, for predicting whether >= C or >= M-class solar flares occurring within 24 hours. We build a real-time forecasting system using these five models, which can achieve classification and probability forecasting. The 10-fold cross-validation sets are generated in chronological order using the full-disk magnetograms provided by the Solar Dynamics Observatory/Helioseismic and Magnetic Imager at 00:00 UT from May 1, 2010, to March 31, 2023. Then after training, validation, and testing our models, we compare the results with the true skill statistic (TSS) and Brier Skill Score (BSS) as assessment metrics. The major results are as follows: (1) There are no statistically significant differences in TSS and BSS performance between models with attention mechanisms and the CNN model. (2) For >= C-class flare prediction, the Recall of the ViT model reaches 0.833, significantly better than that of the CNN model. For >= M-class flare prediction, the Recall of the CNN-ECA and ViT models are 0.799 and 0.855, respectively, which are significantly higher than those of the CNN model. (3) We develop a full-disk solar flare prediction system that has been running since May 1, 2023. By December 31, all five models achieve a TSS of 0.984 for predicting >= C-class flares, with the CNN-SE model demonstrating a BSS of 0.939. For >= M-class flares, the CNN-SE model achieves a TSS of 0.304, while the BSS values for the CNN and CNN-SE models are 0.019 and 0.018, respectively. Additionally, the prediction performance for >= M-class flares on the testing set without No-flare class samples, is similar to that of real-time predictions, validating the good generation performance of the model in real-time forecasting. |
| 学科主题 | 天文学 ; 太阳与太阳系 |
| URL标识 | 查看原文 |
| 出版地 | VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS |
| WOS关键词 | SPACE WEATHER ; MODELS |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001344596000001 |
| 出版者 | SPRINGER |
| 资助机构 | National Natural Science Foundation of China |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/27660]  |
| 专题 | 云南天文台_射电天文研究组 |
| 作者单位 | 1.MailBox 5111, Beijing, 100094, China 2.Yunnan Sino-Malaysian International Joint Laboratory of HF-VHF Advanced Radio Astronomy Technology, Kunming, 650216, China; 3.University of Chinese Academy of Sciences, Beijing, 100049, China; 4.National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China; 5.Yunnan Astronomical Observatory, Chinese Academy of Sciences, Kunming, 650216, China; 6.School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212100, China; |
| 推荐引用方式 GB/T 7714 | Yan, Pengchao,Li, Xuebao,Zheng, Yanfang,et al. A real-time solar flare forecasting system with deep learning methods[J]. ASTROPHYSICS AND SPACE SCIENCE,2024,369(10). |
| APA | Yan, Pengchao.,Li, Xuebao.,Zheng, Yanfang.,董亮.,Yan, Shuainan.,...&Pan, Yexin.(2024).A real-time solar flare forecasting system with deep learning methods.ASTROPHYSICS AND SPACE SCIENCE,369(10). |
| MLA | Yan, Pengchao,et al."A real-time solar flare forecasting system with deep learning methods".ASTROPHYSICS AND SPACE SCIENCE 369.10(2024). |
入库方式： OAI收割
来源：云南天文台
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