Articles | Volume 42, issue 1
https://doi.org/10.5194/angeo-42-91-2024
https://doi.org/10.5194/angeo-42-91-2024
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

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Cited articles

Aminalragia-Giamini, S., Jiggens, P., Anastasiadis, A., Sandberg, I., Aran, A., Vainio, R., Papadimitriou, C., Papaioannou, A., Tsigkanos, A., Paouris, E., Vasalos, G., Paassilta, M., and Dierckxsens, M.: : Prediction of Solar Proton Event Fluence spectra from their Peak flux spectra, J. Space Weather Spac., 10, 1, https://doi.org/10.1051/swsc/2019043, 2020. 
Bai, S. J., Kolter, J. Z., and Koltun, V.: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, ArXiv [preprint], https://doi.org/10.48550/arXiv.1803.01271, 19 April 2018. 
Dieleman, S., van den Oord, A., and Simonyan, K.: The challenge ofrealistic music generation: Modelling raw audio at scale, ArXiv [preprint], https://doi.org/10.48550/arXiv.1806.10474, 26 June 2018. 
Du, Z.: Forecasting the Daily 10.7 cm Solar Radio Flux Using an Autoregressive Model, Sol. Phys., 295, 125, https://doi.org/10.1007/s11207-020-01689-x, 2020. 
Government of Canada: Solar radio flux – archive of measurements, Government of Canada [data set], https://spaceweather.gc.ca/forecast-prevision/solar-solaire/solarflux/sx-5-en.php, last access: 9 April 2024. 
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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.