İyonosfer Parametrelerinin Çok Katmanlı Algılayıcılar ile Kestirimi

Abstract

İyonosferik parametrelerin değişimi, uzay iklimi, haberleşme ve seyrüsefer konularındaki çalışmalarda oldukça önemli bir role sahiptir. Bu çalışmada, derin öğrenme yöntemlerinden olan Çok Katmanlı Algılayıcılar (ÇKA) regresyonu modelinin F2 katmanı kritik frekansı (foF2), tepe elektron yoğunluğunun F2 katmanı yüksekliği (hmF2) ve toplam elektron içeriği (TEC) gibi iyonosfer parametrelerini kestirim performansı analiz edilmiştir. 1 Ocak 2012 ile 31 Aralık 2013 tarihleri arasında, ROME (RO041) digisonde istasyonunun saatlik f0F2 ve hmF2 değerleri ile M0SE00ITA istasyon kodlu Uluslararası GNSS Servisi (IGS) istasyonunun saatlik TEC değerleri kullanılmıştır. Her iki istasyon da birbirine oldukça yakındır ve orta enlem bölgesinde bulunmaktadır. Önerilen yöntemde eğitilecek olan girdi parametreleri, verilerin gözlem periyotları, F10.7 güneş indeksi, jeomanyetik Ap indeksinin saatlik değerleri ve mevcut (t) zamanındaki f0F2, hmF2 ve TEC değerleri ile bunların bir önceki güne ait (t-23) değerleri olarak seçilmiştir. Çıktı değişken ise, bu parametrelerin bir saat ileri (t+1) tahmin değerleridir. 2012 yılına ait veriler, bu modelin eğitilmesi için kullanılmıştır. 2013 yılı verileri üzerinde gerçekleştirilen tahmin çalışmalarının doğruluğu için ortalama kök karesel hata ve korelasyon değerleri hesaplanmış olup, bu değerler tüm yıl, yaz, kış ve ekinoks dönemleri için ayrı ayrı karşılaştırılmıştır. Sonuçlar, önerilen regresyon modelinin kestirim performansının genellikle Kış döneminde daha yüksek, yaz döneminde ise diğer dönemlere görece düşük olduğunu göstermiştir. Analiz edilen tüm dönemlerde elde edilen istatistiksel sonuçlara göre, modelin çoklu iyonosferik parametrelerin tahmininde genel anlamda başarılı olduğu tespit edilmiştir.

Keywords

• iyonosferik parametre kestirimi
• derin öğrenme
• çok katmanlı algılayıcı

The Prediction of Ionospheric Parameters Using Multi-layer Perceptrons

Abstract

The variation of the ionospheric parameters has a crucial role in space weather, communication, and navigation applications. In this research, we analyze the prediction performance of multi-layer perceptron (MLP) regression model, which is one of deep learning algorithms, for the F2-layer critical frequency (f0F2), F2-layer height of the peak electron density (hmF2), and total electron content (TEC). The hourly f0F2 and hmF2 values of ROME (RO041) digisonde and hourly TEC values of an International GNSS Service (IGS) station with site code M0SE00ITA were obtained for the period between January 1, 2012 and 31 December 2013. Both stations are located in the mid-latitude region and are very close to each other. The inputs to be trained in the proposed methods are the observation periods of the data, hourly values of solar index F10.7 and geomagnetic index Ap, the present values of f0F2(t), hmF2(t), TEC(t), and their values at t−23h as separately. The output is the predicted values of parameters at t + 1. The 2012 values of these parameters were used to train the models and they were predicted 1 hour in advance during 2013. The root mean square error (RMSE) and correlation values between observed and predicted data were compared for the whole year, summer, winter, and equinox periods. The results showed that the prediction performance of the proposed regression model is generally higher in the Winter period and lower in the summer period than the other periods. The statistical results obtained in all analyzed periods shown that the model was generally successful in forecasting multiple ionospheric parameters.

Keywords

• Ionospheric parameter prediction
• deep learning
• multi-layer perceptron

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