An Improved Extreme Learning Machine Prediction Model for Ionospheric Total Electron Content

Earth’s ionosphere is an important medium for navigation,communication,and radio wave transmission.Total Electron Content(TEC)is a descriptive quantify for ionospheric research.However,the traditional empirical model could not fully consider the changes of TEC time series,the prediction accuracy level of TEC data performed not high.In this study,an improved Extreme Learning Machine(ELM)model is proposed for ionospheric TEC prediction.Improvements involved the use of Empirical Mode Decomposition(EMD)and a Fuzzy C-Means(FCM)clustering algorithm to pre-process data used as input to the ELM model.The proposed model fully uses the TEC data characteristics and expected to perform better prediction accuracy.TEC measurements provided by the Centre for Orbit Determination in Europe(CODE)were used to evaluate the performance of the improved ELM model in terms of prediction accuracy,applicable latitude,and the number of required training samples.Experimental results produced a Mean Relative Error(MRE)and a Root Mean Square Error(RMSE)of 8.5%and 1.39 TECU,respectively,outperforming the ELM algorithm(RMSE=2.33 TECU and MRE=17.1%).The improved ELM model exhibited particularly high prediction accuracy in mid-latitude regions,with a mean relative error of 7.6%.This value improved further as the number of available training data increased and when 20-doys data were trained,achieving a mean relative error of 4.9%.These results suggest the proposed model offers higher prediction accuracy than conventional algorithms.