| Yazarlar |
Merve Begüm Terzi
|
|
Orhan Arıkan
İhsan Doğramacı Bilkent Üniversitesi, Türkiye |
Doç. Dr. Seçil KARATAY
Kastamonu Üniversitesi, Türkiye |
|
Feza Arıkan
Hacettepe Üniversitesi, Türkiye |
|
Tamara Gulyaeva
|
| Özet |
| In this study, Total Electron Content (TEC) estimated from GPS receivers is used to model the regional and local variability that differs from global activity along with solar and geomagnetic indices. For the automated classification of regional disturbances, a classification technique based on a robust machine learning technique that have found wide spread use, Support Vector Machine (SVM) is proposed. Performance of developed classification technique is demonstrated for midlatitude ionosphere over Anatolia using TEC estimates generated from GPS data provided by Turkish National Permanent GPS Network (TNPGN-Active) for solar maximum year of 2011. As a result of implementing developed classification technique to Global Ionospheric Map (GIM) TEC data, which is provided by the NASA Jet Propulsion Laboratory (JPL), it is shown that SVM can be a suitable learning method to detect anomalies in TEC variations. |
| Anahtar Kelimeler |
| Ionosphere | Kernel functions | Space weather | Support vector machines (SVM) |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayımlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | Living Planet Symposium |
| Kongre Tarihi | 09-05-2016 / 13-05-2016 |
| Basıldığı Ülke | Çek Cumhuriyeti |
| Basıldığı Şehir | Prag |
