GPS-Toplam Elektron İçeriği Tahmininde Regresyon Modellerinin Performansının Karşılaştırılması

İyonosfer, üst atmosferde radyo iletişiminin sağlandığı önemli bir katmandır. İyonosfer atmosferin 50 km ila 1000 km yüksekliği boyunca yer alır. İyonosferin en önemli parametresi olan elektron yoğunluğu, konuma, zamana, mevsimlere, yüksekliğe, güneş, jeomanyetik ve sismik aktiviteye bağlı olarak değişir. Elektron yoğunluğunun ölçülebilir önemli bir miktarı, iyonosferin ve üst atmosferin yapısını araştırmak için kullanılan Toplam Elektron İçeriği’dir (TEİ). TEİ kestiriminde, düşük maliyetli ve yaygın alıcı ağına sahip olan Yerküresel Konumlama Sistemi (YKS) yaygın olarak kullanılır. Bu çalışmada YKS’den kestirilen IONOLAB-TEC verileri kullanılmıştır. TEİ'nin tahmini, Dünya-uzay ve uydudan uyduya iletişim sistemlerini çalıştırmak ve planlamak, TEİ kullanarak deprem haberci sinyallerini oluşturmak ve iyonosferdeki anomalileri tespit etmek için önemli bir olgudur. Bu çalışmada, YKS’den elde edilen IONOLAB-TEC verileri, regresyon modelleri kullanılarak tahmin edilmiştir. Test edilen algoritmalar arasında, Üstel Gauss Süreç Regresyon ve Etkileşimli Lineer Regresyon algoritmalarının, TEC tahmini için oldukça başarılı ve yüksek performanslı bir modeller olduğu gözlenmiştir.

Anahtar Kelimeler:

Toplam elektron içeriği, makine öğrenmesi, tahmin, regresyon, Total electron content, machine learning, prediction, regression

Comparison of the Performance of the Regression Models in GPS-Total Electron Content Prediction

The ionosphere is an important layer that provides radio communication in the upper atmosphere. The ionosphere is located between 50 km and 1000 km above the atmosphere. Electron density, which is the most important parameter of the ionosphere, changes depending on location, time, seasons, altitude, solar, geomagnetic and seismic activity. A significant measurable amount of electron density is Total Electron Content (TEC), which is used to probe the structure of the ionosphere and upper atmosphere. The Global Positioning System (GPS), which has a low cost and widespread receiver network is prominent used in TEC estimation. The IONOLAB-TEC data estimated from GPS is used in this study. Prediction of TEC is important phenomenon to operate and to plan the Earth-space and satellite-to-satellite communication systems, to generate the earthquake precursor signals using TEC and to detect of anomalies in the ionosphere. In this study, IONOLAB-TEC data obtained from GPS is estimated using regression models. Among the tested algorithms, it is observed that the Exponential Gaussian Process Regression and Interactions Linear Regression algorithms are very successful and high-performance models for TEC estimation.

Keywords:

Total electron content, machine learning, prediction, regression,

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