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

Astrophysics and Space Science volume 365, Article number: 99 (2020) Cite this article

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.

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Authors and Affiliations

  1. Department of Geomatics Engineering, Faculty of Engineering and Natural Sciences, Tokat Gaziosmanpasa University, Tokat, Turkey

    Samed Inyurt

  2. Department of Water Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

    Mahsa Hasanpour Kashani

  3. Department of Geomatics Engineering, Ceyhan Engineering Faculty, Cukurova University, Adana, Turkey

    Aliihsan Sekertekin

Authors
  1. Samed Inyurt
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  2. Mahsa Hasanpour Kashani
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  3. Aliihsan Sekertekin
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Correspondence to Samed Inyurt.

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Inyurt, S., Hasanpour Kashani, M. & Sekertekin, A. Ionospheric TEC forecasting using Gaussian Process Regression (GPR) and Multiple Linear Regression (MLR) in Turkey. Astrophys Space Sci 365, 99 (2020). https://doi.org/10.1007/s10509-020-03817-2

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  • DOI: https://doi.org/10.1007/s10509-020-03817-2

Keywords

  • TEC
  • Gaussian process regression
  • Multiple linear regression
  • Forecast