Long-Term Forecasting of Solar Activity Indices Using Neural Networks

Abstract

Using artificial neural networks (ANN), we study the possibility for long-term forecasting of the annual mean Wolf numbers and the monthly average solar radiation flux at 2800 MHz. A feedback ANN with error backpropagation was designed and implemented for this purpose. The software allows one to vary the number of input parameters and neurons and the values of the training parameters. The forecast error is calculated and the actual data are graphically compared to the predicted ones. The annual Wolf number was forecasted directly one year ahead using an auxiliary training (“warming-up”) of the ANN by the previous 18 values of the annual Wolf number entering the training sequence. The prediction efficiency was 92%. Adding the coronal index and the annual solar-radiation flux (i.e., the SF index) to the input parameters leads to some improvement of the forecast. The iterative long-term forecast of the annual Wolf number for 1986-2000 yielded an efficiency of 71%. Using the direct and iterative techniques, the annual Wolf number was forecasted for the rest of the 23rd and the beginning of the 24th cycles (2000-2010).