Annales Geophysicae
Annales Geophysicae
ANGEO
Articles | Volume 42, issue 1
Articles | Volume 42, issue 1
https://doi.org/10.5194/angeo-42-91-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/angeo-42-91-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 42, issue 1
Regular paper
 | 
12 Apr 2024
Regular paper |  | 12 Apr 2024

Deep temporal convolutional networks for F10.7 radiation flux short-term forecasting

Luyao Wang, Hua Zhang, Xiaoxin Zhang, Guangshuai Peng, Zheng Li, and Xiaojun Xu

Viewed

Total article views: 1,563 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,134 353 76 1,563 89 126
  • HTML: 1,134
  • PDF: 353
  • XML: 76
  • Total: 1,563
  • BibTeX: 89
  • EndNote: 126
Views and downloads (calculated since 15 Aug 2023)
Cumulative views and downloads (calculated since 15 Aug 2023)

Viewed (geographical distribution)

Total article views: 1,563 (including HTML, PDF, and XML) Thereof 1,561 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Nov 2025
Download
Short summary
The temporal convolutional network (TCN) approach in deep learning is used to predict the daily value of F10.7. The prediction results for 1–3 d ahead during solar cycle 24 have a high correlation coefficient (R) of 0.98 and a root mean square error (RMSE) of only 5.04–5.18 sfu.
Read more
Share