![]() ![]() It is noticed that the proposed SVR model has well predicted the VTEC values better than NN and IRI-2016 models. The proposed model performance is compared with Neural Networks (NN) model, and International Reference Ionosphere (IRI-2016) model during both LSA and HSA periods. The performance of the proposed SVR model with kernel Gaussian or Radial Basis Function (RBF) is evaluated over the two selected testing periods during the High Solar Activity (HSA) year, 2014 and the Low Solar Activity (LSA) year, 2019. The vertical TEC data estimated from GPS measurements for the entire 24th solar cycle period, 11 years (2009–2019), is considered over Bengaluru and Hyderabad International GNSS Service (IGS) stations. Hence, a supervised ML algorithm such as the Support Vector Regression (SVR) model is proposed to predict TEC over northern equatorial and low latitudinal GNSS stations. Machine Learning (ML) techniques are proven better for ionospheric space weather predictions due to their ability of processing and learning from the available datasets of solar-geophysical data. The ionospheric TEC exhibits a complex spatial–temporal pattern over equatorial and low latitude regions, which are difficult to predict for providing early warning alerts to GNSS users. Ionospheric Total Electron Content (TEC) predominantly affects the radio wave communication and navigation links of Global Navigation Satellite Systems (GNSS). ![]()
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