Ionospheric TEC forecasting using Gaussian Process Regression (GPR) and Multiple Linear Regression (MLR) in Turkey
Yazarlar (3)
Prof. Dr. Samed İNYURT
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
University Of Mohaghegh Ardabili, İran
Çukurova Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | Astrophysics and Space Science |
| Dergi ISSN | 0004-640X Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q4 |
| Makale Dili | İngilizce |
| Basım Tarihi | 06-2020 |
| Cilt No | 365 |
| Sayı | 6 |
| Sayfalar | 1 / 17 |
| DOI Numarası | 10.1007/s10509-020-03817-2 |
| Makale Linki | https://link.springer.com/article/10.1007/s10509-020-03817-2 |
| Özet |
| 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. |
| Anahtar Kelimeler |
| Forecast | Gaussian process regression | Multiple linear regression | TEC |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 32 |
| SCOPUS | 37 |
| Google Scholar | 54 |