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This paper discusses the diurnal and seasonal variations of the F2 layer critical frequency (foF2) and the improvement of performance of the IRI-2016 model in predicting foF2 over three latitudes in different hemispheres during low and high solar activities. We extracted the foF2 data from six ionosonde stations which are Manila (14.7 degrees N, 121.1 degrees E), Yamagawa (31.2 degrees N, 130.6 degrees E), Yakutsk (62.0 degrees N,129.6 degrees E), Townsville (19.6 degrees S, 146.8 degrees E), Hobart (42.9 degrees S, 147.3 degrees E) and Terre Adelie (66.6 degrees S, 140.0 degrees E). The data of both low solar activity (LSA) period and high solar activity (HSA) periods were divided into three seasons as Northern Summer (May, June, July and August), Equinoxes (March, April, September and October) and Northern Winter (November, December, January and February). The present study showed that the IRI-2016 performance is strongly dependent on the solar activity, latitude, season, local time and hemisphere. For both hemispheres, the foF2 values at low latitude station are larger than those at middle latitude station, whereas the foF2 values at middle latitude station are larger than those at high latitude station. The agreement between IRI2016-modelled foF2 and foF2 measurements on all stations selected in the northern hemisphere is best for North Summer and worst for North Winter. For northern hemisphere, the values of relative deviations during both solar activities are largest in high latitudes and smallest in middle latitudes. As for southern hemisphere, the values of relative deviations during LSA are largest in middle latitudes and smallest in high latitudes, whereas the values of relative deviations during HSA are largest in low latitudes and smallest in high latitudes. It is thought that the relative deviations in the observed foF2 values are caused by solar activity that strongly alter chemical and electromagnetic processes in the ionosphere. These results are important for future improvements depending on solar activity and seasons in the IRI model for foF2 values over three latitudes in different hemispheres.

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.