Support Vector Regression model to predict TEC for GNSS signals

• Autorzy: Sivakrishna Kondaveeti , Ratnam Devanaboyina Venkata , Sivavaraprasad Gampala

Identyfikatory

DOI

10.1007/s11600-022-00954-w

• Języki publikacji: EN

Abstrakty

EN

Ionospheric Total Electron Content (TEC) predominantly affects the radio wave communication and navigation links of Global Navigation Satellite Systems (GNSS). The ionospheric TEC exhibits a complex spatial–temporal pattern over equatorial and low latitude regions, which are difficult to predict for providing early warning alerts to GNSS users. Machine Learning (ML) techniques are proven better for ionospheric space weather predictions due to their ability of processing and learning from the available datasets of solar-geophysical data. Hence, a supervised ML algorithm such as the Support Vector Regression (SVR) model is proposed to predict TEC over northern equatorial and low latitudinal GNSS stations. The vertical TEC data estimated from GPS measurements for the entire 24th solar cycle period, 11 years (2009–2019), is considered over Bengaluru and Hyderabad International GNSS Service (IGS) stations. The performance of the proposed SVR model with kernel Gaussian or Radial Basis Function (RBF) is evaluated over the two selected testing periods during the High Solar Activity (HSA) year, 2014 and the Low Solar Activity (LSA) year, 2019. The proposed model performance is compared with Neural Networks (NN) model, and International Reference Ionosphere (IRI-2016) model during both LSA and HSA periods. It is noticed that the proposed SVR model has well predicted the VTEC values better than NN and IRI-2016 models. The experimental results of the SVR model evidenced that it could be an effective tool for predicting TEC over low-latitude and equatorial regions.

Słowa kluczowe

EN

Global Navigation Satellite System; Total Electron Content; IRI-2016 model; Artificial Neural Network model; Support Vector Regression model

PL

globalny system nawigacji satelitarnej; Całkowita zawartość elektronów; Model IRI-2016; model sztucznej sieci neuronowej; model regresji wektorów pomocniczych

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2022
• Tom: Vol. 70, no 6
• Strony: 2827--2836
• Opis fizyczny: Bibliogr. 22 poz.

Twórcy

autor

Sivakrishna Kondaveeti

• Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India
• School of Engineering, Malla Reddy University, Maisammaguda, Dulapally, Hyderabad, Telangana 500100, India

autor

Ratnam Devanaboyina Venkata

• Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

autor

Sivavaraprasad Gampala

• Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).