Time series modeling of ionosphere total electron content using wavelet neural network and hybrid PSO training algorithm

Abstract

In this paper, WNN with PSO training algorithm is used to modeling and prediction of time-dependent ionosphere total electron content (TEC) variations. 2 different combinations of input observations are evaluated. The number of stations used to train of WNN with PSO algorithm selected 20 and 10. In all testing mode, 3 GPS stations with proper distribution are considered as a testing stations. Statistical indicators relative error, dVTEC and correlation coefficient were used to assess the wavelet neural network model. The results of proposed model compared with GPS-TEC and international reference ionosphere 2012 (IRI-2012) TEC. Average relative error computed in 3 test stations are 5.43% with 20 training station and 9.05% with 10 training station. Also the correlation coefficient calculated in 3 test stations are 0.954 with 20 training station and 0.907 with 10 training station. The results of this study show that the WNN with PSO algorithm is a reliable model to predict the temporal variations in the ionosphere.

Keywords

• tec
• wnn
• pso
• gps
• iri-2016