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. The coverage of regional ionosphere maps is determined by thedistribution of ground-based monitoring stations, e.g., GNSS receivers. Sinceionospheric delay has a high spatial correlation, ionosphere map coveragecan be extended using spatial extrapolation methods. This paper proposes asupport vector machine (SVM) to extrapolate the ionosphere map data withsolar and geomagnetic parameters. One year of IGS ionospheric delay map dataover South Korea is used to train the SVM algorithm. Subsequently, 1 monthof ionospheric delay data outside the input data region is estimated. Inaddition to solar and geomagnetic environmental parameters, the ionosphericdelay data from the inner data region are used to estimate the ionosphericdelay data for the outside region. The accuracy evaluation is performed atthree levels of range − 5, 10, and 15 ∘ outside the inner data regions. The extrapolation errors are 0.33 TECU (total electron content unit) forthe 5 ∘ region and 1.95 TECU for the 15 ∘ region. Thesevalues are substantially lower than the GPS Klobuchar model error values.Comparison with another machine learning extrapolation method, the neuralnetwork, shows a substantial improvement of up to 26.7 %.

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