Application of F_10.7 Index Prediction Model Based on BiLSTM-attention and Chinese Autonomous Dataset

The F_10.7 index is an important indicator of solar activity. Accurate predictions of the F_10.7 index can help prevent and mitigate the effects of solar activity on areas such as radio communications, navigation and satellite communications. Based on the properties of the F_10.7 radio flux, the prediction model of F_10.7 based on BiLSTM-Attention is proposed by incorporating an Attention mechanism on the Bidirectional Long Short-Term Memory Network (BiLSTM). The Mean Absolute Error (MAE) on the Canadian DRAO dataset is 5.38, the Mean Absolute Percentage Error (MAPE) is controlled to within 5% and the correlation coefficient (R) reaches 0.987. It has superior prediction performance compared with other RNN models in both short-term and medium-term prediction. A Conversion Average Calibration (CAC) method is proposed to preprocess the F_10.7 data set observed by the Langfang L&S telescope in China. The processed data has high correlation with the DRAO dataset. Based on this dataset the forecasting effectiveness of the RNN series models is compared and analyzed. The experimental results show that both BiLSTM-Attention and BiLSTM models have significant advantages in predicting the F_10.7 index and show excellent predictive performance and good stability. The BiLSTM-Attention model has the highest prediction accuracy when forecasting future first-day data, with MAE and MAPE of 11.10 and 8.66, respectively, and the MAPE is always within 15% in the short- and medium-term forecasts. This shows that the proposed model has high generalization ability and can effectively predict the F_10.7 data set of DRAO and L&S.