空间科学学报2024，Vol.44Issue(2)：251-261,11.DOI:10.11728/cjss2024.02.2023-0040
基于BiLSTM-Attention的F10.7指数预测模型与中国自主数据集的应用
Application of F10.7 Index Prediction Model Based on BiLSTM-attention and Chinese Autonomous Dataset
摘要
Abstract
The F10.7 index is an important indicator of solar activity.Accurate predictions of the F10.7 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 F10.7 radio flux,the prediction model of F10.7 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 Calibra-tion(CAC)method is proposed to preprocess the F10.7 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 F10.7 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 fore-casts.This shows that the proposed model has high generalization ability and can effectively predict the F10.7 data set of DRAO and L&S.关键词
F10.7预报/双向长短时记忆网络/注意力机制/L&S数据集Key words
F10.7 index forecasting/Bidirectional Long Short-Term Memory Network/Attention mechanism/L&S dataset分类
天文与地球科学引用本文复制引用
闫帅楠,郑艳芳,李雪宝,董亮,黄文耿,王晶,闫鹏朝,娄恒瑞,黄徐胜,李哲..基于BiLSTM-Attention的F10.7指数预测模型与中国自主数据集的应用[J].空间科学学报,2024,44(2):251-261,11.基金项目
国家自然科学基金项目(11703009,11803010)和江苏省自然科学基金面上项目(BK20201199)共同资助 （11703009,11803010）
