A neural network-based foF2 model for a single station in the polar cap

Athieno, R.; Jayachandran, P. T.; Themens, David R.

doi:10.1002/2016rs006192

Cited by 30 publications

(28 citation statements)

References 46 publications

(74 reference 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%

Estimation of Ionospheric Critical Plasma Frequencies From GNSS‐TEC Measurements Using Artificial Neural Networks

et al. 2019

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This paper describes a new neural network-based approach to estimate ionospheric critical plasma frequencies (f 0 F2) from Global Navigation Satellite Systems (GNSS)-vertical total electron content (TEC) measurements. The motivation for this work is to provide a method that is realistic and accurate for using GNSS receivers (which are far more commonly available than ionosondes) to acquire f 0 F2 data. Neural networks were employed to train vertical TEC and corresponding f 0 F2 observations respectively obtained from closely located GNSS receivers and ionosondes in various parts of the globe. Available data from 52 pairs of ionosonde-GNSS receiver stations for the 17-year period from 2000 to 2016 were used. Results from this work indicate that the relationship between f 0 F2 and TEC is mostly affected by the seasons, followed by the level of solar activity, and then the local time. Geomagnetic activity was the least significant of the factors investigated. The relationship between f 0 F2 and TEC was also shown to exhibit spatial variation; the variation is less conspicuous for closely located stations. The results also show that there is a good correlation between the f 0 F2 and TEC parameters. The f 0 F2/TEC ratio was generally observed to be lower during enhanced ionospheric ionizations in the day time and higher during reduced ionospheric ionizations in the nights and early mornings. The analysis of errors shows that the model developed in this work (known as the NNT2F2 model) can be used to estimate the f 0 F2 from GNSS-TEC measurements with accuracies of less than 1 MHz. The new approach described in this paper to obtain f 0 F2 based on GNSS-TEC data represents an important contribution in space weather prediction.Plain Language Summary Ionospheric critical plasma frequency (known as f 0 F2 for short) represents the value of radio frequency below which radio signals are reflected by the ionosphere. It is therefore an important information for radio communicators to be able to understand the paths of their radio propagation between transmitters and receivers; f 0 F2 is usually derived from ionosondes/digisondes that are expensive and sparsely located across the globe. On the other hand, Global Navigation Satellite Systems receivers have been used to measure the ionospheric TEC (total electron content), and they are much more abundantly located across the globe. This research presents a new method that is based on the application of artificial neural networks to derive f 0 F2 from TEC. It offers a computer program that can be used on Global Navigation Satellite Systems receivers to derive f 0 F2 values from TEC measurements. This therefore makes f 0 F2 data to be much more spatially available.

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Section: Introductionmentioning

confidence: 99%

Estimation of Ionospheric Critical Plasma Frequencies From GNSS‐TEC Measurements Using Artificial Neural Networks

et al. 2019

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“…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%

A Prediction Model of Ionospheric f_oF₂ Based on Extreme Learning Machine

Bai

Wang

et al. 2018

Radio Science

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The highly nonlinear variation of the ionospheric F 2 layer critical frequency (f o F 2 ) greatly limits the efficiency of communications, radar, and navigation systems that employ high-frequency radio waves. This paper proposes an effective method to predict the f o F 2 using the extreme learning machine (ELM). Compared with the previous neural network model based on feedforward algorithm, the ELM model offers the advantages of faster training speed and less manual intervention. The ELM model is trained with the daily hourly values of f o F 2 at Darwin (12.4°S, 131.5°E) in Australia. The training data are selected from 1995 to 2012, except 1997 and 2000, which includes all periods of quiet and disturbed geomagnetic conditions. The f o F 2 data to verify model performance are selected in 1997, 2000, and 2013, which are low, high, and moderate solar activity years, respectively. The prediction results have shown that the proposed ELM model can achieve faster training process while maintaining the similar accuracy compared with BPNN. In addition, the proposed ELM model is compared with the International Reference Ionosphere model prediction. The ELM model predicts the f o F 2 values more accurately than the International Reference Ionosphere model in low (1997), moderate (2013), and high (2000) solar activity years, as clearly seen on the yearly root-mean-square error. As far as the author's knowledge, this is the first time that the ELM model is applied to predict f o F 2 .Plain Language Summary The spatiotemporal variability of the ionospheric F 2 layer critical frequency (f o F 2 ) affects the efficiency of communications, radar, and navigation systems that employ high-frequency radio waves. Therefore, many researchers have established different kinds of models to predict the changes in f o F 2 based on historical data. However, these studies only focus on how to improve the prediction accuracy of the model. In fact, for a predictive model, it is also crucial to increase the training speed of the model on the premise of ensuring prediction accuracy, especially when dealing with a large amount of data (such as f o F 2 data). For this reason, a modeling method using extreme learning machine (ELM) for the prediction of f o F 2 is proposed in this paper. This proposed method has fast learning speed and good generalization performance. The results show that the proposed method provides a successful prediction of the f o F 2 . Compared with the International Reference Ionosphere model, the average prediction accuracy has increased by 34%. Compared with the backward propagation neural network, the ELM model accelerates the model training speed by 61 times while maintaining the similar predicting accuracy. In summary, the modeling method using ELM for the prediction of the f o F 2 provides a new idea for the modeling problems of the ionospheric parameters.

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“…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%

“…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]. The neural network was first applied to build the foF2 model over the Grahamstown station (33 • S, 26 • E) using the data range set of 1973-1983 [11].…”

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confidence: 99%

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Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations

Zhao

Hé

et al. 2020

Remote Sensing

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The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%-35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly and Weddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. 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]. The neural network was first applied to build the foF2 model over the Grahamstown station (33 • S, 26 • E) using the data range set of 1973-1983 [11]. This model can predict the daily noon foF2 value with a root-mean-square error (RMSE) of 0.95 MHz and the monthly averaged RMSE value was 0.48 MHz. Afterward, this approach has been widely applied in the Asia/Pacific sector [5], equatorial region [12], European region [13] and the auroral zone [14]. These studies showed an improvement in the accuracy of the ANN-based models over the International Reference Ionosphere (IRI) model.With the advancement of data acquisition, more ionospheric measurements have become available for the global modelling of foF2. For instance, Oyeyemi et al. [15] used the foF2 data from 59 globally distributed ionospheric stations to establish th...

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A neural network-based foF2 model for a single station in the polar cap

Cited by 30 publications

References 46 publications

Estimation of Ionospheric Critical Plasma Frequencies From GNSS‐TEC Measurements Using Artificial Neural Networks

Estimation of Ionospheric Critical Plasma Frequencies From GNSS‐TEC Measurements Using Artificial Neural Networks

A Prediction Model of Ionospheric f_oF₂ Based on Extreme Learning Machine

Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations

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A neural network-based foF2 model for a single station in the polar cap

Cited by 30 publications

References 46 publications

Estimation of Ionospheric Critical Plasma Frequencies From GNSS‐TEC Measurements Using Artificial Neural Networks

Estimation of Ionospheric Critical Plasma Frequencies From GNSS‐TEC Measurements Using Artificial Neural Networks

A Prediction Model of Ionospheric foF2 Based on Extreme Learning Machine

Advanced Machine Learning Optimized by The Genetic Algorithm in Ionospheric Models Using Long-Term Multi-Instrument Observations

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A Prediction Model of Ionospheric f_oF₂ Based on Extreme Learning Machine