Performance evaluation of neural network TEC forecasting models over equatorial low-latitude Indian GNSS stationOACSCD
Performance evaluation of neural network TEC forecasting models over equatorial low-latitude Indian GNSS station
G. Sivavaraprasad;V.S. Deepika;D. SreenivasaRao;M. Ravi Kumar;M. Sridhar
Department of Electronics and Communication Engineering, Koneru Lakshamaiah Education Foundation, K L Deemed to be University, Vaddeswaram, Guntur District, 522502, Andhra Pradesh, IndiaDepartment of Electronics and Communication Engineering, Koneru Lakshamaiah Education Foundation, K L Deemed to be University, Vaddeswaram, Guntur District, 522502, Andhra Pradesh, IndiaDepartment of Electronics and Communication Engineering, Koneru Lakshamaiah Education Foundation, K L Deemed to be University, Vaddeswaram, Guntur District, 522502, Andhra Pradesh, IndiaDepartment of Electronics and Communication Engineering, Koneru Lakshamaiah Education Foundation, K L Deemed to be University, Vaddeswaram, Guntur District, 522502, Andhra Pradesh, IndiaDepartment of Electronics and Communication Engineering, Koneru Lakshamaiah Education Foundation, K L Deemed to be University, Vaddeswaram, Guntur District, 522502, Andhra Pradesh, India
Global Positioning System (GPS)Global navigation satellite systems (GNSS)Total electron content (TEC)International reference ionosphere (IRI)Neural networks
Global Positioning System (GPS)Global navigation satellite systems (GNSS)Total electron content (TEC)International reference ionosphere (IRI)Neural networks
《大地测量与地球动力学（英文版）》 2020 (3)
192-201,10
The presentwork has been carried out under the research project titled "Implementation of Deep Learning Algorithms to Develop Web based Ionospheric Time Delays Forecasting System over Indian Region using Ground based GNSS and NAVigation with Indian Constellation (NAVIC) observations" sponsored by Science& Engineering Research Board (SERB) (A statutory body of the Department of Science&Technology,Governmentof India,NewDelhi, India, vide sanction order No: ECR/2018/001701, and Department of Science and Technology, New Delhi, India for funding this research through SR/FST/ESI-130/2013(C) FIST program.
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