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Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method

文献类型：期刊论文

作者Zhang, Wanting2,3; Zhao, Xinhua1,2,4; Feng, Xueshang2; Liu, Cheng’ao5; Xiang NB(向南彬)6; Li, Zheng7; Lu, Wei2,3
刊名UNIVERSE
出版日期2022-01
卷号8期号:1
关键词solar radio flux time series forecast long short-term memory
DOI10.3390/universe8010030
产权排序第6完成单位
文献子类Article
英文摘要As an important index of solar activity, the 10.7-cm solar radio flux (F-10.7) can indicate changes in the solar EUV radiation, which plays an important role in the relationship between the Sun and the Earth. Therefore, it is valuable to study and forecast F-10.7. In this study, the long short-term memory (LSTM) method in machine learning is used to predict the daily value of F-10.7. The F-10.7 series from 1947 to 2019 are used. Among them, the data during 1947-1995 are adopted as the training dataset, and the data during 1996-2019 (solar cycles 23 and 24) are adopted as the test dataset. The fourfold cross validation method is used to group the training set for multiple validations. We find that the root mean square error (RMSE) of the prediction results is only 6.20~6.35 sfu, and the correlation coefficient (R) is as high as 0.9883~0.9889. The overall prediction accuracy of the LSTM method is equivalent to those of the widely used autoregressive (AR) and backpropagation neural network (BP) models. Especially for 2-day and 3-day forecasts, the LSTM model is slightly better. All this demonstrates the potentiality of the LSTM method in the real-time forecasting of F-10.7 in future.
学科主题天文学 ; 射电天文学 ; 射电天文方法 ; 太阳与太阳系
URL标识查看原文
出版地ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
WOS关键词CM ; F10.7
资助项目N/A
WOS研究方向Astronomy & Astrophysics ; Physics
语种英语
出版者MDPI
WOS记录号WOS:000757157500001
资助机构N/A
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/24907]  
专题云南天文台_抚仙湖太阳观测站
作者单位1.Yading Space Weather Science Center, Daocheng 627750, China;
2.State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China;
3.School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China;
4.CAS Key Laboratory of Solar Activity, National Astronomical Observatories, Beijing 100101, China;
5.CAEIT, Beijing 100041, China;
6.Yunnan Observatories, Chinese Academy of Sciences, Kunming 650011, China;
7.Institute of Space Weather, Nanjing University of Information Science & Technology, Nanjing 210044, China
推荐引用方式
GB/T 7714		 Zhang, Wanting,Zhao, Xinhua,Feng, Xueshang,et al. Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method[J]. UNIVERSE,2022,8(1).
APA Zhang, Wanting.,Zhao, Xinhua.,Feng, Xueshang.,Liu, Cheng’ao.,向南彬.,...&Lu, Wei.(2022).Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method.UNIVERSE,8(1).
MLA Zhang, Wanting,et al."Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method".UNIVERSE 8.1(2022).

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