Journal ArticleDaily Predictions of F10.7 and F30 Solar Indices With Deep LearningWang, Zhenduo [Department of Computer Science New Jersey Institute of Technology Newark NJ USA]; Abduallah, Yasser [Department of Computer Science New Jersey Institute of Technology Newark NJ USA]; Wang, Jason_T L [Department of Computer Science New Jersey Institute of Technology Newark NJ USA] (ORCID:0000000224861097); Wang, Haimin [Big Bear Solar Observatory New Jersey Institute of Technology Big Bear City CA USA]; Xu, Yan [Big Bear Solar Observatory New Jersey Institute of Technology Big Bear City CA USA]; Yurchyshyn, Vasyl [Big Bear Solar Observatory New Jersey Institute of Technology Big Bear City CA USA]; Oria, Vincent [Department of Computer Science New Jersey Institute of Technology Newark NJ USA]; Alobaid, Khalid A [College of Applied Computer Sciences King Saud University Riyadh Saudi Arabia] (ORCID:0009000747312772); Bai, Xiaoli [Department of Mechanical and Aerospace Engineering Rutgers University Piscataway NJ USA] (ORCID:0000000160708201)<title>Abstract</title> <p>The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 and 30 cm, respectively, which are key indicators of solar activity. F10.7 is valuable for explaining the impact of solar ultraviolet (UV) radiation on the upper atmosphere of Earth, while F30 is more sensitive and could improve the reaction of thermospheric density to solar stimulation. In this study, we present a new deep learning model, named the Solar Index Network, or SINet for short, to predict daily values of the F10.7 and F30 solar indices. The SINet model is designed to make medium‐term predictions of the index values (1–60 days in advance). The observed data used for SINet training were taken from the National Oceanic and Atmospheric Administration as well as Toyokawa and Nobeyama facilities. Our experimental results show that SINet performs better than five closely related statistical and deep learning methods for the prediction of F10.7. Furthermore, to our knowledge, this is the first time deep learning has been used to predict the F30 solar index.</p>Wiley2026-02-0110674739Journal of Geophysical Research: Space Physics13122169-9380https://doi.org/10.1029/2025JA0348682300341; 2504860; 2206886National Science Foundation