عنوان مقاله [English]
The ionosphere is the upper part of the Earth's atmosphere, which is considered to be approximately 70 to 1000 km above the Earth's surface. Ionosphere modeling has been one of the goals of spatial geodesy since 1970. In many ionosphere modeling using satellite measurements such as GPS, total electron content (TEC) are used as observational input data. In recent years, modeling and prediction of the TEC have been considered by researchers with methods that have high speed and accuracy. One of the branches that has been able to show good capabilities in the field of estimation and modeling is machine learning methods (ML). Machine learning includes fuzzy inference systems (FIS), artificial neural networks (ANNs), genetic algorithms (GAs), support vector machines (SVMs), and evolutionary communications (ECs). Since 1993, with the advancement of computer technology, many new and hybrid algorithms, such as the adaptive neuro-fuzzy inference system (ANFIS), have been developed in ML. Another new effective approach in ML is the support vector regression (SVR) method. The SVR is a kernel-based ML method for classification and regression in which the risk of incorrect classification is minimized. The structure of an SVR network has a lot in common with the ANN, and the main difference is practically in the way of the training algorithm. In general, this method is divided into linear and nonlinear modes.
In this paper, the TEC of the ionosphere is modeled and evaluated with ML models. Support vector regression (SVR) and artificial neural network (ANN) methods are used for local TEC modeling. In both models, the latitude and longitude of the GPS stations, day of the year (DOY), hours, AP, KP, DST, and F10.7 are considered an input vectors. Also, the value of VTEC is considered as the output of the models. The main innovation of this paper is in evaluating the effect of different physical parameters on the accuracy of ML models. Using observations of 15 GPS stations in the northwest of Iran from 193 to 228 in 2012, new models are evaluated. Also, the results of the new models are compared with the results of the global ionosphere map (GIM), the IRI2016, and NeQuick empirical models in two internal and one external control station. Statistical indices of root mean square error (RMSE), relative error, dVTEC, and correlation coefficient are used to evaluate the error of the models. Sensitivity analysis of SVR and ANN models to input parameters is performed and the importance of each physical parameter in spatio-temporal modeling of the ionosphere is investigated. The results obtained from this paper show that in both high and low geomagnetic and solar activities, the SVR model in internal control stations has a higher accuracy than other models. But at the external control station, the error of the SVR model is much higher than other models. Determining the parameters of the kernel function using observations at the territory of the studied network is the reason. Also, the sensitivity of SVR and ANN models is increased to the physical parameters F10.7, KP, DST, and AP, respectively. For precise local ionosphere modeling, the effect of these parameters must also be considered.