Deep Learning Prediction Method for f₀F₂ Parameters Based on the Ionospheric Parameter Similarity Features

The ionosphere is an important component of the solar terrestrial space environment, and the critical frequency f₀F₂ parameter is one of the most important and complex ionospheric parameters. The changes in f₀F₂ parameters will have a certain degree of impact on communication, navigation, radar and other technologies, so predicting f₀F₂ parameters has important research significance and application value. This article proposes a deep learning prediction method that integrates the characteristics of f₀F₂ parameter changes. A model combining bidirectional long short-term memory neural network BiLSTM network and ionospheric parameter similarity features is used to predict the ionospheric critical frequency f₀F₂ parameter 24 hours in advance. The results show that the average relative error of BiLSTM combined with ionospheric parameter similarity model in predicting f₀F₂ parameters is about 8%～10%. Compared with the Long Short-Term Memory (LSTM) model, the average relative error has decreased by about 6% to 7%, while compared with the BiLSTM model, the average relative error has decreased by about 4% to 5%. The prediction results of the f₀F₂ parameter for different latitude detection stations show that as the latitude decreases, the difficulty of predicting the f₀F₂ parameter increases, the prediction errors of the three models increase, and the prediction accuracy decreases. The analysis of the prediction results of f₀F₂ parameters during geomagnetic storms shows that the predictive performance of the three models will be affected to varying degrees during the occurrence of geomagnetic storms, and the prediction error will increase. Compared to the calm period, the average relative error of the three models has increased by about 1% to 4%. During geomagnetic storms, compared with LSTM and BiLSTM models, BiLSTM combined with ionospheric parameter similarity feature models has better predictive performance for f₀F₂ parameters and better predictive performance. This method can also be applied to the prediction research of other ionospheric parameters such as Total Electron Content (TEC), h_mF₂, etc., and has a very broad application prospect.