A NEURAL NETWORK MODEL OF THE RELATIVISTIC ELECTRON FLUX AT GEOSYNCHRONOUS ORBIT

A neural network has been developed to model the temporal variations of relativistic (> 3 MeV) electrons at geosynchronous orbit based on model inputs consisting of 10 consecutive days of the daily sum of the planetary magnetic index SIGMA-Kp. The neural network (in essence, a nonlinear prediction filter) consists of three layers of neurons, containing 10 neurons in the input layer, 6 neurons in a hidden layer, and 1 output neuron. The output is a prediction of the daily-averaged electron flux for the tenth day. The neural network was trained using 62 days of data from July 1, 1984, through August 31, 1984, from the SEE spectrometer on the geosynchronous spacecraft 1982-019. The performance of the model was measured by comparing model outputs with measured fluxes over a 6-year period from April 19, 1982, to June 4, 1988. For the entire data set the rms logarithmic error of the neural network is 0.76, and the average logarithmic error is 0.58. The neural network is essentially zero biased, and for accumulation intervals of 3 days or longer the average logarithmic error is less than 0.1. The neural network provides results that are significantly more accurate than those from linear prediction filters. The model has been used to simulate conditions which are rarely observed in nature, such as long periods of quiet (SIGMA-Kp = 0) and ideal impulses. It has also been used to make reasonably accurate day-ahead forecasts of the relativistic electron flux at geosynchronous orbit.