Adaptive forecasting of high-energy electron flux at geostationary orbit using ADALINE neural network

Abstract

High-energy electron flux increases in the recovery phase after the space weather events such as a coronal mass ejection. High-energy electrons can penetrate circuits deeply and the penetration could lead to deep dielectric charging. The forecast of high-energy electron flux is vital in providing warning information for spacecraft operations. We investigate an adaptive predictor based on ADALINE neural network. The predictor can forecast the trend of the daily variations in high-energy electrons. The predictor was trained with the dataset of ten years from 1998 to 2008. We obtained the prediction efficiency approximately 0.6 each year except the first learning year 1998. Furthermore, the predictor can adapt to the changes for the satellite's location. Our model succeeded in forecasting the high-energy electron flux 24 hours ahead. © 2009 Springer Berlin Heidelberg.