An improved NeQuick-G global ionospheric TEC model with a machine learning approach
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Earth and Planetary Sciences
Link
https://link.springer.com/content/pdf/10.1007/s10291-023-01426-4.pdf
Reference14 articles.
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2. Brunini C, Azpilicueta F, Gende M, Camilion E, Aragon-Angel A, Hernandez-Pajares M, Juan M, Sanz J, Salazar D (2011) Ground-and space-based GPS data ingestion into the NeQuick model. J Geod 85(12):931–939
3. Cesaroni C, Spogli L, Aragon-Angel A, Fiocca M, Dear V, De Franceschi G, Romano V (2020) Neural network based model for global total electron content forecasting. J Space Weather Space Clim 10:1–18
4. Chen C, Wu Z, Ban P, Sun S, Xu Z, Zhao Z (2010) Diurnal specification of the ionospheric f0F2 parameter using a support vector machine. Radio Sci 45(5):1–13. https://doi.org/10.1029/2010RS004393
5. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
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