Regional application of ANFIS in ionosphere time series prediction at severe solar activity period

Abstract

The ionosphere and its activities play an important role in radio communications, space science and positioning. In this paper, we present efficiency of the adaptive neuro-fuzzy inference system (ANFIS) in ionosphere time series prediction at severe solar activity periods. The high accuracy prediction of the ionosphere total electron content (TEC) using ANFIS is the main purpose of the paper. Also, investigation of the daily, monthly and annually temporal behavior of the ionosphere is another goal of the paper. Three GPS stations (ANKR, ANTL and ZONG) from Turkish permanent GPS network (TPGN) are selected for analysis. These stations are at mid-latitudes. To predict and analyze ionospheric temporal behavior, observations of the 2014–2015 are selected. The index of solar activity in these 2 years is the maximum. Training of ANFIS is done by back-propagation (BP) algorithm. In order to analyze the accuracy of the proposed method, testing step has been performed in different days, months and seasons and compared with the global ionosphere map (GIM) TEC as a traditional ionosphere model, as well as, international reference ionosphere 2016 (IRI2016) and artificial neural network (ANN) models. To validate the results, root mean square error (RMSE), correlation coefficient, relative error, dTEC = |TECGPS-TECMODEL| and residual histogram are used. At testing step, the maximum correlation coefficient obtained 0.869 for the ANFIS. Also, the minimum RMSE of 3.131 TECU is computed for ANFIS at testing step. Using the ANFIS model, the daily, monthly and annual variations of the ionosphere time series are revealed with high accuracy. The results show that the ANFIS model is a reliable and high-precision alternative to the conventional ionosphere models in Turkish region.