Ionosphere time series modeling using adaptive neuro-fuzzy inference system and principal component analysis

Abstract

The total electron content (TEC) is one of the most important parameters for studying the behavior of the ionosphere. The global ionosphere maps (GIMs) can be used to study the TEC time series variations. The time resolution of the GIM-TEC is 2 h, whereas the frequency of the ionospheric temporal behavior can be less than 2 h. To solve this problem, we present a new method for ionosphere time series modeling and prediction in Iran. The adaptive neuro-fuzzy inference system (ANFIS) and principal component analysis are combined to model the TEC of the ionosphere. In fact, the observations are decomposed into principal components before entering to the ANFIS network and only a few main components are used for training the network. The main advantage of this combination is to increase the accuracy of the results and reduce the time of convergence to achieve an optimal solution. To evaluate the proposed method, we used observations of a Tehran GNSS station in 2016 and 2017. The root-mean-square error, correlation coefficient, and dVTEC = |VTECGPS − VTECmodel| were used to assess the accuracy of the proposed method. Also, all results are compared with the International Reference Ionosphere 2016 (IRI2016), GIM-TEC, and artificial neural networks (ANNs) ionosphere models. The results indicate a 1 to 4.72 TECU improvement in the temporal resolution of TEC modeling with the proposed method, compared to the IRI2016, GIM, and ANNs in the Iranian region.