The 10.7‐cm radio flux multistep forecasting based on empirical mode decomposition and back propagation neural network

Abstract

The 10.7‐cm solar radio flux (F10.7) is among the most widely used indices of solar activity, whose prediction plays a vital role in the field of meteorology and aerospace. In this paper, a novel approach for a specific amalgamation of back propagation neural network (BPNN) and empirical mode decomposition (EMD) is proposed, which is called EMD‐BP, aimed at forecasting the daily F10.7 values 1–27 days ahead. The daily F10.7 values, which are highly nonlinear and unstable time series, are transformed into a series of subsignals with different frequency components by EMD and trained by the BPNN. For estimating the performance of the description EMD‐BP, the support vector regression (SVR), Long Short‐Term Memory neural network and BPNN as the reference models are analyzed in different conditions. The results indicate that the proposed model improves the F10.7 forecasting within 27 days, compared with the common machine learning algorithms and transport model. Furthermore, a tremendous decline of forecast error is determined in high‐level solar activity, in contrast with SVR and BPNN. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.