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Fine-grained Solar Flare Forecasting Based on the Hybrid Convolutional Neural Networks
Deng, Zheng1,2,3; Wang, Feng1,3; Deng, Hui1,3; Tan, Lei.1; Deng LH(邓林华)4; Feng, Song2
发表期刊ASTROPHYSICAL JOURNAL
2021-12
卷号922期号:2
DOI10.3847/1538-4357/ac2b2b
产权排序第4完成单位
收录类别SCI
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%.

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
项目资助者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
语种英语
学科领域天文学 ; 太阳与太阳系
学科门类理学 ; 理学::天文学
文章类型Article
出版者IOP Publishing Ltd
出版地TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
ISSN0004-637X
URL查看原文
WOS记录号WOS:000725852900001
WOS研究方向Astronomy & Astrophysics
WOS类目Astronomy & Astrophysics
引用统计
被引频次:9[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
版本出版稿
条目标识符http://ir.ynao.ac.cn/handle/114a53/24706
专题抚仙湖太阳观测和研究基地
通讯作者Wang, Feng
作者单位1.Center For Astrophysics, Guangzhou University, Guangzhou 510006, People's Republic of China; fengwang@gzhu.edu.cn, denghui@gzhu.edu.cn;
2.Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, People's Republic of China;
3.Great Bay Center, National Astronomical Data Center, Guangzhou, Guangdong, 510006, People's Republic of China;
4.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, People's Republic of China
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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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