Fine-grained Solar Flare Forecasting Based on the Hybrid Convolutional Neural Networks | |
Deng, Zheng1,2,3; Wang, Feng1,3; Deng, Hui1,3; Tan, Lei.3; Deng LH(邓林华)4; Feng, Song2 | |
刊名 | ASTROPHYSICAL JOURNAL |
2021-12 | |
卷号 | 922期号:2 |
ISSN号 | 0004-637X |
DOI | 10.3847/1538-4357/ac2b2b |
产权排序 | 第4完成单位 |
文献子类 | Article |
英文摘要 | Improving the performance of solar flare forecasting is a hot topic in the solar physics research field. Deep learning has been considered a promising approach to perform solar flare forecasting in recent years. We first used the generative adversarial networks (GAN) technique augmenting sample data to balance samples with different flare classes. We then proposed a hybrid convolutional neural network (CNN) model (M) for forecasting flare eruption in a solar cycle. Based on this model, we further investigated the effects of the rising and declining phases for flare forecasting. Two CNN models, i.e., M (rp) and M (dp), were presented to forecast solar flare eruptions in the rising phase and declining phase of solar cycle 24, respectively. A series of testing results proved the following. (1) Sample balance is critical for the stability of the CNN model. The augmented data generated by GAN effectively improved the stability of the forecast model. (2) For C-class, M-class, and X-class flare forecasting using Solar Dynamics Observatory line-of-sight magnetograms, the means of the true skill statistics (TSS) scores of M are 0.646, 0.653, and 0.762, which improved by 20.1%, 22.3%, and 38.0% compared with previous studies. (3) It is valuable to separately model the flare forecasts in the rising and declining phases of a solar cycle. Compared with model M, the means of the TSS scores for No-flare, C-class, M-class, and X-class flare forecasting of the M (rp) improved by 5.9%, 9.4%, 17.9%, and 13.1%, and those of the M (dp) improved by 1.5%, 2.6%, 11.5%, and 12.2%. |
学科主题 | 天文学 ; 太阳与太阳系 |
URL标识 | 查看原文 |
出版地 | TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND |
资助项目 | National SKA Program of China[2020SKA0110300] ; National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC)[U1831204] ; National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC)[U1931141] ; Chinese Academy of Sciences (CAS)Chinese Academy of Sciences[U1831204] ; Chinese Academy of Sciences (CAS)Chinese Academy of Sciences[U1931141] ; Funds for International Cooperation and Exchange of the National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC)[11961141001] ; Astronomical Big Data Joint Research Center ; Chinese Academy of SciencesChinese Academy of Sciences ; Alibaba Cloud |
WOS研究方向 | Astronomy & Astrophysics |
语种 | 英语 |
出版者 | IOP Publishing Ltd |
WOS记录号 | WOS:000725852900001 |
资助机构 | National SKA Program of China[2020SKA0110300] ; National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC)[U1831204, U1931141] ; Chinese Academy of Sciences (CAS)Chinese Academy of Sciences[U1831204, U1931141] ; Funds for International Cooperation and Exchange of the National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC)[11961141001] ; Astronomical Big Data Joint Research Center ; Chinese Academy of SciencesChinese Academy of Sciences ; Alibaba Cloud |
内容类型 | 期刊论文 |
版本 | 出版稿 |
源URL | [http://ir.ynao.ac.cn/handle/114a53/24706] |
专题 | 云南天文台_抚仙湖太阳观测站 |
通讯作者 | Wang, Feng |
作者单位 | 1.Great Bay Center, National Astronomical Data Center, Guangzhou, Guangdong, 510006, People's Republic of China; 2.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, People's Republic of China; 3.Center For Astrophysics, Guangzhou University, Guangzhou 510006, People's Republic of China; fengwang@gzhu.edu.cn, denghui@gzhu.edu.cn; 4.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, People's Republic of China |
推荐引用方式 GB/T 7714 | Deng, Zheng,Wang, Feng,Deng, Hui,et al. Fine-grained Solar Flare Forecasting Based on the Hybrid Convolutional Neural Networks[J]. ASTROPHYSICAL JOURNAL,2021,922(2). |
APA | Deng, Zheng,Wang, Feng,Deng, Hui,Tan, Lei.,Deng LH,&Feng, Song.(2021).Fine-grained Solar Flare Forecasting Based on the Hybrid Convolutional Neural Networks.ASTROPHYSICAL JOURNAL,922(2). |
MLA | Deng, Zheng,et al."Fine-grained Solar Flare Forecasting Based on the Hybrid Convolutional Neural Networks".ASTROPHYSICAL JOURNAL 922.2(2021). |
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