中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 云南天文台
  3. 中国科学院云南天文台
  4. 射电天文研究组
A novel solar flare forecast model with deep convolution neural network and one-against-rest approach

文献类型：期刊论文

作者Zhang, Shunhuang8; Zheng, Yanfang8; Li, Xuebao8; Ye, Hongwei8; Dong L(董亮)6,7; Huang, Xusheng8; Yan, Pengchao8; Li, Xuefeng8; Wei, Jinfang8; Xiang, Changtian8
刊名ADVANCES IN SPACE RESEARCH
出版日期2024-10-01
卷号74期号:7页码:3467-3480
关键词Active regions Magnetic fields Solar flare prediction Deep learning
ISSN号0273-1177
DOI10.1016/j.asr.2024.06.035
产权排序第2完成单位
文献子类Article
英文摘要We present a novel deep Convolutional Neural Network model with one-against-rest approach (OAR-CNN) and modify the hybrid Convolutional Neural Network (H-CNN) model of Zheng et al. (2019) for multiclass flare prediction to forecast whether an active region generates multiclass flare within 24 h. Additionally, in the OAR-CNN and H-CNN models, we employ the decision strategies of majority voting and probability threshold, respectively, comparing the prediction outcomes of these two strategies. Our models undergo training and testing on the same 10 cross-validation datasets as employed by Zheng et al. (2019), and then compare the results with previous studies using forecast verification metrics, with a focus on the true skill statistic (TSS). The major results are summarized as follows. (1) This is the first attempt to utilize the decision strategies of majority voting and probability threshold in the OAR-CNN model for multiclass solar flare prediction. (2) In both the OAR-CNN and H-CNN models, the predictive results with the probability threshold decision strategy are higher than those with majority voting across all six classes (i.e., No-flare, C, M, X, C, and M class), except for a slight decrease in the C class in the OAR-CNN model. (3) The OAR-CNN and modified H-CNN models with the probability threshold decision strategy demonstrate comparable statistical scores across all categories and outperform previous studies. (4) In the prediction of four-class flare, our proposed OAR-CNN model with the probability threshold decision strategy achieves relatively high mean TSS scores of 0.744, 0.429, 0.567, and 0.630 for No-flare, C, M, and X class, respectively, surpassing or comparable to results from prior studies. Furthermore, our model achieves high TSS scores of 0.744 +/- 0.042 for C-class and 0.764 +/- 0.089 for M-class predictions. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
学科主题天文学 ; 太阳与太阳系
URL标识查看原文
出版地125 London Wall, London, ENGLAND
WOS关键词SPACE-WEATHER
资助项目National Natural Science Foundation of China[11703009]; National Natural Science Foundation of China[11803010]; Natural Science Foundation of Jiangsu Province, China[BK20170566]; Natural Science Foundation of Jiangsu Province, China[BK20201199]; Qing Lan Project; National Natural Science Astronomy Joint Fund[U2031133]; Kunming Foreign (International) Cooperation Base Project[GHJD-2021022]
WOS研究方向Engineering ; Astronomy & Astrophysics ; Geology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:001298102000001
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China[11703009, 11803010] ; Natural Science Foundation of Jiangsu Province, China[BK20170566, BK20201199] ; Qing Lan Project ; National Natural Science Astronomy Joint Fund[U2031133] ; Kunming Foreign (International) Cooperation Base Project[GHJD-2021022]
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/27579]  
专题云南天文台_射电天文研究组
作者单位1.MailBox 5111, Beijing 100094, Peoples R China
2.Yunnan Sino Malaysian Int Joint Lab HF VHF Adv Rad, Kunming 650216, Peoples R China;
3.Chinese Acad Sci, Yunnan Astron Observ, Kunming 650216, Peoples R China;
4.Jiangsu Univ Sci & Technol, Sch Comp Sci, Zhenjiang 212100, Peoples R China;
5.MailBox 5111, Beijing 100094, China;
6.Yunnan Sino-Malaysian International Joint Laboratory of HF-VHF Advanced Radio Astronomy Technology, Kunming 650216, China;
7.Yunnan Astronomical Observatory, Chinese Academy of Sciences, Kunming 650216, China;
8.School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang 212100, China;
推荐引用方式
GB/T 7714		 Zhang, Shunhuang,Zheng, Yanfang,Li, Xuebao,et al. A novel solar flare forecast model with deep convolution neural network and one-against-rest approach[J]. ADVANCES IN SPACE RESEARCH,2024,74(7):3467-3480.
APA Zhang, Shunhuang.,Zheng, Yanfang.,Li, Xuebao.,Ye, Hongwei.,董亮.,...&Pan, Yexin.(2024).A novel solar flare forecast model with deep convolution neural network and one-against-rest approach.ADVANCES IN SPACE RESEARCH,74(7),3467-3480.
MLA Zhang, Shunhuang,et al."A novel solar flare forecast model with deep convolution neural network and one-against-rest approach".ADVANCES IN SPACE RESEARCH 74.7(2024):3467-3480.

入库方式： OAI收割

来源：云南天文台

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。