Ionospheric TEC forecasting using Gaussian Process Regression (GPR) and Multiple Linear Regression (MLR) in Turkey

• Published in: Astrophysics and space science Vol. 365; no. 6
• Main Authors: Inyurt, Samed, Hasanpour Kashani, Mahsa, Sekertekin, Aliihsan
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2020; Springer Nature B.V
• Subjects: Astrobiology; Astronomy; Astrophysics; Astrophysics and Astroparticles; Cosmology; Gaussian process; Global navigation satellite system; Ionospheric electron content; Navigation satellites; Navigation systems; Observations and Techniques; Original Article; Physics; Physics and Astronomy; Regression analysis; Regression models; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Stations; Total Electron Content; Turkey; Gaussian process regression; TEC; Multiple linear regression; Forecast
• ISSN: 0004-640X; 1572-946X
• DOI: 10.1007/s10509-020-03817-2

This study aims to predict daily ionospheric Total Electron Content (TEC) using Gaussian Process Regression (GPR) model and Multiple Linear Regression (MLR). In this case, daily TEC values from 2015 to 2017 of two Global Navigation Satellite System (GNSS) stations were collected in Turkey. The performance of the GPR model was compared with the classical MLR model using Taylor diagrams and relative error graphs. Six models with various input parameters were performed for both GPR and MLR techniques. The results showed that although the models perform similarly, the GPR model estimated the TEC values more precisely at one and two days ahead. Therefore, the GPR model is recommended to forecast the TEC values at the corresponding GNSS stations over Turkey.