Machine learning based approach for modeling and forecasting of GPS-TEC during diverse solar phase periods

The ionosphere is a dispersive medium and it is the most important source of error affecting radio communi-cations. As a result, precise improvements in ionospheric Total Electron Content (TEC) predictions and early warning alerts are critical for space-based radio communication and navigation systems services. In the present study, we examined the Global Positioning System (GPS)-derived TEC observations of Bangalore station (Geog. Lat. 13.02 degrees N and Geog. Long. 77.57 degrees E) during the 24th solar cycle, which extends the 11 years from 2009 to 2019. Based on Solar Radio Flux (sfu) and seasonal features, machine learning frameworks have been developed to forecast monthly/hourly GPS-TEC values. Motivated by the preliminary results, the problem of traditional approaches for predicting ionospheric GPS-TEC values and improvements using the machine learning approach is discussed. The Kernel extreme learning machine (KELM) model has characteristics of accuracy and good generalization performance compared with the traditional method of Holt-Winters (HW) and (Auto Regressive Moving Average (ARMA). The experimental results show that the statistical validations of the KELM model are superior to the other statistical models, with average Mean Absolute Error (MAE) and Root Mean Square Error values, which is of 0.94 and 1.305 TECU (KELM), 1.70 and 2.29 TECU (ARMA) and 2.91 and 3.86 TECU (HW) during different phases of the solar cycle. In addition, the proposed model is validated by comparison with global ionospheric models (International Reference Ionosphere (IRI)-2016 and Global Ionosphere Map (GIM). The results show that the proposed approach is superior to other alternatives using prediction accuracy. This paper outlines a machine learning framework for space weather predictions and recommends KELM classifier could be used for other query functions in Active Learning (AL) practice approaches and other datasets of the Global Navigation Satellite System (GNSS).