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

Inyurt S., Hasanpour Kashani M., ŞEKERTEKİN A.

ASTROPHYSICS AND SPACE SCIENCE, vol.365, no.6, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 365 Issue: 6
  • Publication Date: 2020
  • Doi Number: 10.1007/s10509-020-03817-2
  • Journal Name: ASTROPHYSICS AND SPACE SCIENCE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Communication Abstracts, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: TEC, Gaussian process regression, Multiple linear regression, Forecast, NEURAL-NETWORK
  • Çukurova University Affiliated: Yes

Abstract

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.