A Prediction Method of Ionospheric hmF2 Based on Machine Learning

Abstract

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.

Prediction of global ionospheric TEC using attention based bidirectional long short-term memory and gated recurrent unit

“…The largest naturally occurring error probability of GNSS is rectifiable with the help of broadcasting or empirical models of ionosphere which are generally utilized to diminish the ionospheric delay. The models have the efficiency to achieve better accuracy and meet the needs of the user based on a single frequency and high positioning [8], [9]. The ionospheric time delay is proportional to the ionosphere's TEC.…”