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Quantitative Prediction of High-Energy Electron Integral Flux at Geostationary Orbit Based on Deep Learning
Wei, Lihang; Zhong, Qiuzhen; Lin, Ruilin; Wang, Jingjing; Liu, Siqing; Cao, Yong; Zhong, QZ (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Beijing, Peoples R China.; Zhong, QZ (reprint author), Univ Chinese Acad Sci, Beijing, Peoples R China.
Department空间环境部
Source PublicationSPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
2018
Volume16Issue:7Pages:903
ISSN1542-7390
Language英语
AbstractThe deep learning method of long short-term memory (LSTM) is applied to develop a model to predict the daily >2-MeV electron integral flux 1 day ahead at geostationary orbit. The inputs to the model include geomagnetic and solar wind parameters such as Kp, Ap, Dst, solar wind speed, magnetopause subsolar distance, and the value of >2-MeV electron integral flux itself over the previous five consecutive days. The model is trained on the data from the periods 1999-2007 and 2011-2016, and the efficiency of the model is tested on the 2008-2010 period. We experiment with different input combinations and find that when the model takes daily >2-MeV electron integral flux, daily averaged magnetopause subsolar distance, and daily summed Kp index as inputs, the prediction efficiencies for 2008, 2009, and 2010 are 0.833, 0.896, and 0.911, respectively. This value reaches 0.900 for 2008, when hourly >2-MeV electron integral flux, hourly magnetopause subsolar distance, and daily summed Kp index are taken as inputs, with training on the remaining data from 19 June 2003 to 13 April 2010. The prediction efficiencies of the persistence model and the 27-order autoregressive model for the same tested time period are 0.679 and 0.743, respectively. Therefore, the model developed based on the LSTM method can improve the prediction efficiency significantly for daily >2-MeV electron integral flux 1 day ahead at geostationary orbit. me earlier models
Indexed BySCI
Document Type期刊论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6391
Collection空间环境部
Corresponding AuthorZhong, QZ (reprint author), Chinese Acad Sci, Natl Space Sci Ctr, Beijing, Peoples R China.; Zhong, QZ (reprint author), Univ Chinese Acad Sci, Beijing, Peoples R China.
Recommended Citation
GB/T 7714
Wei, Lihang,Zhong, Qiuzhen,Lin, Ruilin,et al. Quantitative Prediction of High-Energy Electron Integral Flux at Geostationary Orbit Based on Deep Learning[J]. SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS,2018,16(7):903.
APA Wei, Lihang.,Zhong, Qiuzhen.,Lin, Ruilin.,Wang, Jingjing.,Liu, Siqing.,...&Zhong, QZ .(2018).Quantitative Prediction of High-Energy Electron Integral Flux at Geostationary Orbit Based on Deep Learning.SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS,16(7),903.
MLA Wei, Lihang,et al."Quantitative Prediction of High-Energy Electron Integral Flux at Geostationary Orbit Based on Deep Learning".SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS 16.7(2018):903.
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