Deep Learning Algorithm-Based Prediction of Maximum Layer Height in Ionosphere F2

The F2 layer is located in the uppermost part of the Earth's ionosphere, which plays a crucial role in the reflection of radio signals, the propagation of radio waves, and radio communication, etc. The height of the peak electron density in the F2 layer (hmF2) is an important parameter of the F2 layer, and its variation can reflect the Earth's magnetic field and the solar activity, so that in the field of space communication and in the study of the solar activity, hmF2 can provide an important reference. In summary, accurate prediction of hmF2 is of great importance for improving radio communication and solar activity prediction studies. The existing International Reference Ionospheric (IRI) model can provide users with comprehensive information and guidance on ionospheric conditions, but it still has some limitations in its ability to predict hmF2 parameters with high accuracy in the short term. In this paper, after deeply analyzing the ionospheric parameter data obtained from the vertical survey instrument, we carefully construct and train several accurate and efficient neural network-based ionospheric parameter prediction models. The accurate prediction of hmF2 parameters is finally realized, and the prediction effect is greatly improved compared with that of IRI.