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Support Vector Regression model to predict TEC for GNSS signals

Sivakrishna Kondaveeti, Ratnam Devanaboyina Venkata, Sivavaraprasad Gampala

Acta Geophysica

2022

Vol. 70, no 6

2827--2836

Ionospheric Total Electron Content (TEC) predominantly affects the radio wave communication and navigation links of Global Navigation Satellite Systems (GNSS). 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. 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. 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. 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. 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 proposed model performance is compared with Neural Networks (NN) model, and International Reference Ionosphere (IRI-2016) model during both LSA and HSA periods. It is noticed that the proposed SVR model has well predicted the VTEC values better than NN and IRI-2016 models. The experimental results of the SVR model evidenced that it could be an effective tool for predicting TEC over low-latitude and equatorial regions.

Performance analysis of IRI 2016 model TEC predictions over Northern and Southern Hemispheric IGS stations during descending phase of solar cycle 24

Nibigira Jean de Dieu, Sivavaraprasad G., Ratnam D. Venkata

Acta Geophysica

2021

Vol. 69, no. 4

1509--1527

Global Positioning System (GPS) is an efective tool for monitoring the Earth’s ionosphere. This paper concerns with temporal and spatial variations of ionospheric total electron content (TEC) at RAMO, Israel (geographic coordinates: 30.597o N, 34.76o E; geomagnetic coordinates: 27.17o N, 112.40o E), and ZAMB, Zambia (geographic coordinates: 15.42o S, 28.31o E; geomagnetic coordinates: 16.98o S, 98.67o E) for the descending phase of solar cycle-24. The VTEC estimated from GPS measurements and VTEC values modeled from the IRI-2016 model are obtained over both the GPS stations, i.e., RAMO station, in Northern Hemisphere (NH) and ZAMB station in Southern Hemisphere (SH). The diurnal, seasonal, annual, and solar cycle variations in TEC are investigated during 2016–2018. Also, a comparative study is performed between VTEC derived from GPS observations and International Reference Ionosphere-2016 (IRI-2016) model using the statistical analysis. It has been observed that the observed and modeled maximum VTEC decreases with the declining phase of solar cycle-24 over both the stations. The semiannual patterns are noticed in VTEC values of both the IRI-2016 model and GPS observations for all the years, i.e., 2016–2018. At RAMO station, seasonal analysis depicted a year-wise decrease in maximum TEC as follows March Equinox (Mar-Equ), September Equinox (Sep-Equ), December Solstice (Dec-Sol), and June Solstice (Jun Sol). It is observed from the monthly average estimations of the IRI-2016 model that it has relatively more overestimations of VTEC values over RAMO station in NH than over ZAMB in SH during 2016–2018. However, the IRI-2016 model has underestimated the GPS-VTEC values from June–September 2018 over NH, RAMO station. The root-mean-square error (RMSE) values of the IRI-2016 model delineate that the model has more RMSE during March Equinox than September Equinox, whereas these RMSEs are recorded high over NH (RAMO) than SH (ZAMB). At RAMO, the IRI-2016 model has shown high RMSE values during the June solstice compared to the December solstice. On the other hand, at ZAMB, the highest RMSE values are observed during the December solstice than June solstice. Ionolab-TEC and GIM-TEC also considered over both the stations for the analysis. The IRI-2016 model predictions are in good agreement with GPS-VTEC values over SH (ZAMB) compared to NH (RAMO).