Application of LSTM Neural Network in F_10.7 Solar Radio Flux Mid-term Forecast

The F_10.7 index is an important input parameter for the empirical models of atmospheric density, and its prediction accuracy directly affects the accuracy of spacecraft orbit prediction. The solar activity exhibited an average of 11 years on a long-term scale and a 27-day periodic variation on a short-term scale. Based on this observational fact, a l Long and Short Term Memory (LSTM) recurrent neural network method is proposed to conduct the mid-term forecast of F_10.7 index for the next 27 days. Using a continuous long period of F_10.7 data as training data, the LSTM neural network training is constructed, and the upper and lower bounds of model parameters based on empirical formula are determined. The method of trial and error is used to select the optimal model parameters, and the prediction models to predict solar activity of high and low years F_10.7 index in the next 27 days are constructed. The results show that the average relative error of the 27th day F_10.7 index forecast for solar activity in the high year can reach about 10%, and can reach 2% or less in the low year. In 1998, the correlation coefficient between the predicted value of the F_10.7 index on the 27th day and the measured value was 0.60.