An Explainable Dynamic Prediction Method for Ionospheric foF2 Based on Machine Learning

Wang, Jian; Qiao, Yu; Shi, Yafei; Liu, Yiran; Yang, Cheng

doi:10.3390/rs15051256

Cited by 10 publications

(3 citation statements)

References 55 publications

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“…Unlike the black-box algorithm, the model parameters determined using SML methods have explainable and transparent meanings [21]. For example, SML algorithms can be used to solve specific functional analytic expressions, which deep learning algorithms such as artificial neural networks cannot do [22]. Therefore, statistical machine learning is widely used to model ionospheric parameters.…”

Section: Methodsmentioning

confidence: 99%

A Prediction Method of Ionospheric hmF2 Based on Machine Learning

et al. 2023

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The ionospheric F2 layer is the essential layer in the propagation of high-frequency radio waves, and the peak electron density height of the ionospheric F2 layer (hmF2) is one of the important parameters. To improve the predicted accuracy of hmF2 for further improving the ability of HF skywave propagation prediction and communication frequency selection, we present an interpretable long-term prediction model of hmF2 using the statistical machine learning (SML) method. Taking Moscow station as an example, this method has been tested using the ionospheric observation data from August 2011 to October 2016. Only by inputting sunspot number, month, and universal time into the proposed model can the predicted value of hmF2 be obtained for the corresponding time. Finally, we compare the predicted results of the proposed model with those of the International Reference Ionospheric (IRI) model to verify its stability and reliability. The result shows that, compared with the IRI model, the predicted average statistical RMSE decreased by 5.20 km, and RRMSE decreased by 1.78%. This method is expected to provide ionospheric parameter prediction accuracy on a global scale.

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Section: Methodsmentioning

confidence: 99%

A Prediction Method of Ionospheric hmF2 Based on Machine Learning

et al. 2023

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“…The data of TEC used in this work can be downloaded from Global Ionospheric Radio Observatory website (https://giro.uml.edu/didbase/scaled.php) and Community Coordinated Modeling Center (http://irimodel.org/). The produced data file of the paper is available at Wang et al (2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

Wang,

Liu,

Shi

2023

Space Weather

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In order to achieve more accurate spatial reconstruction of ionospheric total electron content (TEC) and promote improved satellite positioning and ranging applications, a high accuracy spatial reconstruction (HASR) method for TEC is proposed based on the surface theory. The core theory of this method is as follows: (a) Any surface can be uniquely determined by its first and second fundamental quantities; (b) By direct difference approximation, differential equations are transformed into algebraic equations to solve Gauss equations faster. At the same time, taking parts of Europe as an example, the proposed HASR method is used to determine the correlation coefficients and the number of iterations of the model by using the relative root mean square error (RRMSE) as the evaluation criterion. The statistical results show that the TEC predicted by the HASR method is highly consistent with the actual observed values of ionospheric observation stations, and the prediction RRMSE is 9.75%. Compared with the Kriging interpolation with scale factor, the prediction accuracy of the HASR method is improved by 8.5%. We hope this method can provide ideas for the spatial reconstruction of other ionospheric parameters and further promote the realization of complete and accurate space weather forecast.

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“…That is to say, the prediction of MUF is indirectly achieved through the prediction of ionospheric parameters such as foF2 and M3000F2. Methods for short-term prediction of ionospheric parameters include neural network methods [12,13], machine learning methods [14,15], empirical orthogonal function analysis [16], etc. Wang et al [17] used the Volterra filter method to forecast M3000F2 and performed regional reconstruction to achieve accurate MUF forecasting due to the ITU model.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Forecasting Method for High-Frequency Broadcast MUF Based on LSTM

Ji,

He,

et al. 2024

Atmosphere

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This paper proposes a short-term forecasting method for high-frequency broadcast Maximum Usable Frequency (MUF) based on Long Short-Term Memory (LSTM) to meet the demand for refined and precise high-frequency broadcast coverage. Based on the existing infrastructure of broadcast and television stations, we established an experimental verification system to collect data for approximately three years. Two links were selected based on data quality: Urumqi to Lhasa and Lanzhou to Lhasa. A short-term forecast of MUF was conducted using the data from these two links. Comparison and analysis were conducted between the forecasting results of our model and those of the REC533 model. Our proposed method outperforms the REC533 forecasting results overall, with a reduction in root mean square error (RMSE) of 0.66 MHz and an improvement in forecast accuracy of 14.77%. The comparison result demonstrates the feasibility and accuracy of our model.

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An Explainable Dynamic Prediction Method for Ionospheric foF2 Based on Machine Learning

Cited by 10 publications

References 55 publications

A Prediction Method of Ionospheric hmF2 Based on Machine Learning

A Prediction Method of Ionospheric hmF2 Based on Machine Learning

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

A Short-Term Forecasting Method for High-Frequency Broadcast MUF Based on LSTM

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