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Ionospheric single‐station TEC short‐term forecast using RBF neural network
Author(s) -
Huang Z.,
Yuan H.
Publication year - 2014
Publication title -
radio science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 84
eISSN - 1944-799X
pISSN - 0048-6604
DOI - 10.1002/2013rs005247
Subject(s) - tec , ionosphere , artificial neural network , term (time) , meteorology , geodesy , remote sensing , computer science , environmental science , geology , geophysics , artificial intelligence , geography , physics , astronomy
In this article a radial basis function (RBF) neural network improved by Gaussian mixture model is developed to be used for forecasting ionospheric 30 min total electron content (TEC) data given the merits of its nonlinear modeling capacity. In order to understand more about the response of developed network model with respect to stations situated at different latitude, estimated TEC overhead of GPS ground stations BJFS (39.61°N, 115.89°E), WUHN (30.53°N, 114.36°E), and KUNM (25.03°N, 102.80°E) for 6 months in 2011 are used for training data set, validating data and test data set of RBF network model. The performance of the trained model is evaluated at a set of criteria. Our results show that the predicted TEC is in good agreement with observations with mean relative error of about 9% and root‐mean‐square error of less than 5 total electron content unit, 1 TECU = 10 16 el m −2 . Our comparison further indicates that RBF network offers a powerful and reliable tool for the design of ionospheric TEC forecast.