2017
DOI: 10.1002/2016rs006192
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A neural network-based foF2 model for a single station in the polar cap

Abstract: A neural network (NN) model has been developed for the critical frequency of the F2 layer (foF2) at Resolute (74.70°N, 265.10°E) using data obtained from the Space Physics Interactive Data Resource (no longer available) for the period between 1975 and 1995. This model is a first step toward addressing the discrepancies of the International Reference Ionosphere (IRI) foF2 or peak electron density (NmF2) at high latitudes recently presented by Themens et al. (2014). The performance of the NN model has been evalu… Show more

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Cited by 31 publications
(28 citation statements)
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“…Neural networks trained with adequate historic data have been established to be ideal methods for the predicting nonlinear ionospheric parameters (Habarulema et al, ; Mckinnell & Poole, ). As a result, the neural network approach has been used in the prediction of some ionospheric parameters such as TEC and f 0 F2 (A et al, ; Athieno et al, ; Habarulema et al, ; Maruyama & Nakamura, ; Nakamura et al, ; Okoh et al, ; Pietrella & Perrone, ).…”
Section: Introductionmentioning
confidence: 99%
“…Neural networks trained with adequate historic data have been established to be ideal methods for the predicting nonlinear ionospheric parameters (Habarulema et al, ; Mckinnell & Poole, ). As a result, the neural network approach has been used in the prediction of some ionospheric parameters such as TEC and f 0 F2 (A et al, ; Athieno et al, ; Habarulema et al, ; Maruyama & Nakamura, ; Nakamura et al, ; Okoh et al, ; Pietrella & Perrone, ).…”
Section: Introductionmentioning
confidence: 99%
“…It is very significant to predict f o F 2 accurately with the help of the existing data for the success of high-frequency communications, radar, and navigation systems. There have been several attempts to model the ionospheric f o F 2 , including popular methods such as the International Reference Ionosphere (IRI; Bilitza et al, 2014;Bilitza & Reinisch, 2008), backward propagation neural network (BPNN) model (Athieno et al, 2017;Mckinnell & Oyeyemi, 2009Oyeyemi et al, 2005Oyeyemi et al, , 2006Oyeyemi & Mckinnell, 2008;Oyeyemi & Poole, 2004;Williscroft & Poole, 1996), and support vector machine technique (Ban et al, 2011;Chen et al, 2010). The IRI model, which was put forward by the Committee on Space Research and the International Union of Radio Science (URSI), is the best known and globally recognized empirical model.…”
Section: Introductionmentioning
confidence: 99%
“…The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data.Remote Sens. 2020, 12, 866 2 of 17 space-borne and ground-based instruments and data processed techniques, more attention has been paid to the development and improvement of these ionospheric models [3].In the previous studies, the artificial neural network (ANN), nonlinear least squares and AdaBoost techniques have been used to predict ionospheric variations with a satisfactory accuracy [4][5][6][7]. Among them, the ANN technique has proven to be a successful tool in the modeling of ionospheric variations as well as solving the forecast problems in many geophysical applications over a single station, regional area and global scale [3,[8][9][10].…”
mentioning
confidence: 99%
“…Remote Sens. 2020, 12, 866 2 of 17 space-borne and ground-based instruments and data processed techniques, more attention has been paid to the development and improvement of these ionospheric models [3].…”
mentioning
confidence: 99%
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