Prediction of Equatorial Electrojet Based on the Neural Network during Quiet Time

Abstract

In this study, BP neural network technology was used to predict the variations of Equatorial Electrojet (EEJ) of Indian sector, Peruvian sector and CHAMP satellite during the magnetic quiet pe-riod after 2008. The neural network training data is the corresponding observation data of EEJ during the period of magnetic quiet from 2000 to 2007. The input parameters are day of year, local time, solar zenith angle, solar activity index (<italic>F</italic>_10.7), lunar age and satellite geographic longitude, and the output parameters are EEJ. The EEJ prediction results are statistically analyzed and compared with the observation results. Results show that: BP neural network has good ability in predicting the variations of EEJ in the event, and the prediction results can reflect the important distribution characteristics of EEJ; there is very good correlation between the predicted values and the observed values of EEJ, and the correlation coefficient between the observed values and the predicted values of geomagnetic sta-tions can reach over 85%. In addition, the prediction results of BP neural network model are compared with those of Yamazakis empirical model, and the performance of BP neural network is equivalent with Yamazakis empirical model. The results show that BP neural network has excellent performance in predicting EEJ variations during quiet period, and has a good application prospect.