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

被引：0
作者：
Samed Inyurt
Mahsa Hasanpour Kashani
Aliihsan Sekertekin
机构：
[1] Tokat Gaziosmanpasa University,Department of Geomatics Engineering, Faculty of Engineering and Natural Sciences
[2] University of Mohaghegh Ardabili,Department of Water Engineering, Faculty of Agriculture and Natural Resources
[3] Cukurova University,Department of Geomatics Engineering, Ceyhan Engineering Faculty
来源：
Astrophysics and Space Science | 2020年 / 365卷
关键词：
TEC; Gaussian process regression; Multiple linear regression; Forecast;
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学科分类号：
摘要：
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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