基于深度学习的中国区域电离层f0F2短期预报方法OA

As a key parameter of the ionosphere,the critical frequency of the F2 layer of the iono-sphere(f0F2)is of great significance for ensuring the stable operation of systems such as high-frequency radar and short-wave communication.This paper proposes a short-term forecasting method for the iono-spheric f0F2 based on deep learning.By using the Bidirectional Long Short-term Memory model with at-tention mechanism(BiLSTM-Attention)algorithm and combining the observed values of the ionosphe-ric f0F2 at the ionosonde station for the previous 7 days,Universal Time(UT),solar activity index,and geomagnetic activity index as inputs,the forecasting of the ionospheric f0F2 in the Chinese region is real-ized.The results of the comparative analysis of the model show that:The forecasting errors for low-lati-tude stations were significantly higher than those for mid-latitude stations.The BiLSTM-Attention mod-el demonstrated superior performance,followed by the Long Short-Term Memory(LSTM)model.Com-pared to the International Reference Ionosphere(IRI)model,the BiLSTM-Attention model achieved a 44.2％reduction in Root Mean Square Error(RMSE),47％decrease in Mean Absolute Error(MAE),and 21.3％improvement in the Coefficient of Determination(R2).During geomagnetic storms,the BiLSTM-Attention model successfully captured the negative storm effects(characterized by f0F2 depletion)in China's regional ionosphere,showing excellent consistency with observational data.However,even when operating in storm mode,the IRI model still exhibited noticeable deviations between predicted and observed f0F2 values.As the forecasting window extended from 1 hour to 24 hours,the model errors showed a systematic increasing trend:RMSE rose from 0.99 MHz to 2.05 MHz,MAE increased from 0.69 MHz to 1.57 MHz,while R2 decreased from 0.93 to 0.75.Relevant research provides high-precision iono-spheric parameter forecasting support for space weather warning and short-wave communication system optimization.