F10.7 Daily Forecast Using LSTM Combined With VMD Method

Abstract

The F_10.7 solar radiation flux is a well-known parameter that is closely linked to solar activity, serving as a key index for measuring the level of solar activity. In this study, the Variational Mode Decomposition (VMD) and Long Short-term Memory (LSTM) network are combined to construct a VMD-LSTM model for predicting F_10.7 values. The F_10.7 sequence is decomposed into several intrinsic mode functions (IMF) by VMD, then the LSTM neural network is utilized to forecast each IMF. All IMF prediction results are aggregated to obtain the final F_10.7 value. The data sets from 1957 to 2008 are used for training and the data sets from 2009 to 2019 are used for testing. The results show that the VMD-LSTM model achieves an annual average root mean square error of only 4.47 sfu and an annual average correlation coefficient (R) of 0.99 during solar cycle 24, which is significantly better than the accuracy of the LSTM model (W. Zhang et al., 2022, https://doi.org/10.3390/universe8010030), the AR model (Du, 2020, https://doi.org/10.1007/s11207-020-01689-x), and the BP model (Xiao et al., 2017, https://doi.org/10.11728/cjss2017.01.001). The VMD-LSTM model exhibits strong predictive capability for the F_10.7 index during solar cycle 24.