Articles | Volume 37, issue 1
Regular paper
31 Jan 2019
Regular paper |  | 31 Jan 2019

Extending the coverage area of regional ionosphere maps using a support vector machine algorithm

Mingyu Kim and Jeongrae Kim

Related subject area

Subject: Earth's ionosphere & aeronomy | Keywords: Modelling and forecasting
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Cited articles

Akhoondzadeh, M.: Support vector machines for TEC seismo-ionospheric anomalies detection, Ann. Geophys., 31, 173–186,, 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,, 2011. 
Borovsky, J. E. and Denton, M. H.: Differences between CME-driven storms and CIR-driven storms, J. Geophys. Res., 111, A07S08,, 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,, 2010. 
Cristianini, N.: Support vector and kernel machines, Tutorial at the 18th Int. Conf. Mach. Learn., 2001. 
Short summary
Spatial extrapolation of an ionosphere TEC map was carried out using a SVM learning algorithm. There has been much research on the temporal extrapolation or prediction of TEC time series, but the spatial extrapolation has rarely been attempted. Some researchers have performed simultaneous extrapolation both in time and in spatial domains, but this research covers the spatial extrapolation only by using an inner TEC map. This spatial TEC extrapolation can be useful for small countries.