Quantitative Prediction of High-Energy Electron Integral Flux at Geostationary Orbit Based on Deep Learning-Reference-Cited by-同舟云学术

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Quantitative Prediction of High-Energy Electron Integral Flux at Geostationary Orbit Based on Deep Learning

Published:2018-07 Issue:7 Volume:16 Page:903-916
ISSN:1542-7390
Container-title:Space Weather
language:en
Short-container-title:Space Weather

Author:

Wei Lihang¹²^ORCID,Zhong Qiuzhen¹²,Lin Ruilin¹,Wang Jingjing¹^ORCID,Liu Siqing¹²^ORCID,Cao Yong³

Affiliation:

1. National Space Science Center, Chinese Academy of Sciences; Beijing People's Republic of China

2. University of Chinese Academy of Sciences; Beijing People's Republic of China

3. Harbin Institute of Technology Shenzhen Graduate School; Shenzhen People's Republic of China

Funder

Shenzhen Technology Project

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

Atmospheric Science

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1029/2018SW001829/fullpdf

Reference45 articles.

1. Recurrent geomagnetic storms and relativistic electron enhancements in the outer magnetosphere: ISTP coordinated measurements;Baker;Journal of Geophysical Research,1997

2. Linear prediction filter analysis of relativistic electron properties at 6.6 RE;Baker;Journal of Geophysical Research,1990

3. Coronal mass ejections, magnetic clouds, and relativistic magnetospheric electron events: ISTP;Baker;Journal of Geophysical Research,1998

4. Learning long-term dependencies with gradient descent is difficult;Bengio;IEEE Transactions on Neural Networks,1994

5. Online NARMAX model for electron fluxes at GEO;Boynton;Annales Geophysicae,2015

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