Articles | Volume 37, issue 1
https://doi.org/10.5194/angeo-37-77-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.Extending the coverage area of regional ionosphere maps using a support vector machine algorithm
Related subject area
Subject: Earth's ionosphere & aeronomy | Keywords: Modelling and forecasting
Modeling total electron content derived from radio occultation measurements by COSMIC satellites over the African region
Analysis of different propagation models for the estimation of the topside ionosphere and plasmasphere with an ensemble Kalman filter
Ann. Geophys., 38, 1203–1215,
2020Ann. Geophys., 38, 1171–1189,
2020Cited articles
Akhoondzadeh, M.: Support vector machines for TEC seismo-ionospheric
anomalies detection, Ann. Geophys., 31, 173–186,
https://doi.org/10.5194/angeo-31-173-2013, 2013.
Ban, P. P., Sun, S. J., Chen, C., and Zhao, Z. W.: Forecasting of
low-latitude storm-time ionospheric f0F2 using support vector machine,
Radio Sci., 46, 1–9, https://doi.org/10.1029/2010RS004633, 2011.
Borovsky, J. E. and Denton, M. H.: Differences between CME-driven storms and
CIR-driven storms, J. Geophys. Res., 111, A07S08, https://doi.org/10.1029/2005JA011447,
2006.
Chen, C., Wu, Z. S., Ban, P. P., Sun, S. J., Xu, Z. W., and Zhao, Z. W.:
Diurnal specification of the ionospheric f0F2 parameter using a support
vector machine, Radio Sci., 45, 1–13, https://doi.org/10.1029/2010RS004393, 2010.
Cristianini, N.: Support vector and kernel machines, Tutorial at the 18th
Int. Conf. Mach. Learn., 2001.