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 = |VTEC_GPS − VTEC_model| 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.

中文翻译：

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基于自适应神经模糊推理系统的电离层时间序列建模和主成分分析

总电子含量（TEC）是研究电离层行为的最重要参数之一。全球电离层图（GIM）可用于研究TEC时间序列的变化。GIM-TEC的时间分辨率为2 h，而电离层时间行为的频率可以小于2 h。为了解决这个问题，我们提出了一种用于伊朗电离层时间序列建模和预测的新方法。自适应神经模糊推理系统（ANFIS）和主成分分析相结合，对电离层的TEC进行建模。实际上，观察结果在进入ANFIS网络之前被分解为主要成分，并且仅使用了几个主要成分来训练网络。这种组合的主要优点是可以提高结果的准确性，并减少收敛时间，以实现最佳解决方案。为了评估所提出的方法，我们使用了2016年和2017年德黑兰GNSS台站的观测结果。均方根误差，相关系数和dVTEC = | VTEC_GPS  − VTEC_模型| 被用来评估所提方法的准确性。此外，将所有结果与国际参考电离层2016（IRI2016），GIM-TEC和人工神经网络（ANN）电离层模型进行比较。结果表明，与伊朗地区的IRI2016，GIM和ANN相比，该方法在TEC建模的时间分辨率上提高了1到4.72 TECU。