Prediction of VLF Sub-Ionospheric Wave Propagation Using Nonlinear System Identification

Abstract

Very low frequency (VLF) waves have been used as a powerful tool to monitor and study the lower ionosphere (D/E region). In this paper, nonlinear physical processes of VLF signals propagation can be well represented by nonlinear autoregressive with exogenous input neural network (NARXNN) model. Further, a study of NARXNN model to predict the daily nighttime mean amplitude of VLF propagation wave to recognize the ionospheric perturbation along the great circle path. The NARXNN model is powerful in predicting time series data and suitable representations of a variation of nonlinear models. The daily input variables of various physical parameters with the time interval from 15 March 2014 to 26 May 2016 were used to build prediction model. The results of the built models are performing reasonably good for one-step ahead (OSA) predictions of the daily nighttime of VLF electric field amplitude. The NARXNN model has good performance for predicting the VLF amplitude variation for different latitude paths.