A Prediction Model of Ionospheric foF2 Based on Extreme Learning Machine

Abstract

AbstractThe highly nonlinear variation of the ionospheric F2 layer critical frequency (foF2) greatly limits the efficiency of communications, radar, and navigation systems that employ high‐frequency radio waves. This paper proposes an effective method to predict the foF2 using the extreme learning machine (ELM). Compared with the previous neural network model based on feedforward algorithm, the ELM model offers the advantages of faster training speed and less manual intervention. The ELM model is trained with the daily hourly values of foF2 at Darwin (12.4°S, 131.5°E) in Australia. The training data are selected from 1995 to 2012, except 1997 and 2000, which includes all periods of quiet and disturbed geomagnetic conditions. The foF2 data to verify model performance are selected in 1997, 2000, and 2013, which are low, high, and moderate solar activity years, respectively. The prediction results have shown that the proposed ELM model can achieve faster training process while maintaining the similar accuracy compared with BPNN. In addition, the proposed ELM model is compared with the International Reference Ionosphere model prediction. The ELM model predicts the foF2 values more accurately than the International Reference Ionosphere model in low (1997), moderate (2013), and high (2000) solar activity years, as clearly seen on the yearly root‐mean‐square error. As far as the author's knowledge, this is the first time that the ELM model is applied to predict foF2.