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

Data sets

Solar radio flux – archive of measurements Government of Canada https://spaceweather.gc.ca/forecast-prevision/solar-solaire/solarflux/sx-5-en.php

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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.
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