Predictions of solar activity cycles 25 and 26 using non-linear autoregressive exogenous neural networks

Abstract

This study presents new prediction models of the 11-yr solar activity cycles (SC) 25 and 26 based on multiple activity indicator parameters. The developed models are based on the use of non-linear autoregressive exogenous (NARX) neural network approach. The training period of the NARX model is from July 1749 to December 2019. The considered activity indicator parameters are the monthly sunspot number time series (SSN), the flare occurence frequency, the 10.7-cm solar radio flux, and the total solar irradiance (TSI). The neural network models are fed by these parameters independently and the prediction results are compared and verified. The obtained training, validation, and prediction results show that our models are accurate with an accuracy of about 90 per cent in the prediction of peak activity values. The current models produce the dual-peak maximum (Gnevyshev gap) very well. Based on the obtained results, the expected solar peaks in terms of SSN (monthly averaged smoothed) of the solar cycles 25 and 26 are R-SSN = 116.6 (February 2025) and R-SSN = 113.25 (October 2036), respectively. The expected time durations of SC 25 and SC 26 cycles are 9.2 and 11 yr, respectively. The activity levels of SC 25 and 26 are expected to be very close and similar to or weaker than SC 24. This suggests that these two cycles are at the minimum level of the Gleissberg cycle. A comparison with other reported studies shows that our results based on the NARX model are in good agreement.