We propose a new method for spatio-temporal modeling of the ionospheric total electron content (TEC) at severe solar activity periods using a support vector machine (SVM). Using the observations from 37 GPS stations of the Iranian permanent GPS network (IPGN) in 2013–2014, the new model has been evaluated. Observations of 33 stations are used for training. Also, four stations are selected to test and evaluate the new model. In the 2 years under evaluation, the solar activity index is the maximum. The new model results are compared with the adaptive neuro-fuzzy inference system (ANFIS) and the artificial neural network (ANN) as other soft computing algorithms. Training of ANFIS and ANN is done with the error back-propagation (BP) method. The new model has been evaluated on different days, months, and seasons. To validate the results, the root mean square error (RMSE), correlation coefficients, residual histograms, relative errors, and standard deviations are used. Also, all the results are compared with the TEC of the global ionosphere map (GIM) as the traditional ionospheric model. For the SVM, GIM, ANFIS, and ANN models, the average RMSE at test stations are 3.642, 6.723, 4.844, and 5.011 TECU, respectively. Also, the average correlation coefficients of the four models evaluated at the test stations are calculated as 0.910, 0.742, 0.863, and 0.841, respectively. The maximum and minimum standard deviations for the SVM model obtained are 4.064 and 3.075 TECU, respectively. The standard deviations of the GIM, ANFIS, and ANN models are greater than the standard deviation of the SVM. Evaluations show that the SVM has high accuracy in modeling the temporal and spatial variations of the ionospheric TEC during periods of severe solar activity. Also, the analysis performed in precise point positioning (PPP) shows a higher accuracy of the new model compared to the other three models at the test stations. The results show that the SVM model is an accurate and reliable alternative to conventional ionospheric models for modeling the temporal and spatial variations of the TEC in the Iranian region.

我们提出了一种使用支持​​向量机 (SVM) 在严重太阳活动期间对电离层总电子含量 (TEC) 进行时空建模的新方法。利用 2013-2014 年伊朗永久 GPS 网络 (IPGN) 的 37 个 GPS 站的观测结果，对新模型进行了评估。33 个台站的观测用于培训。此外，还选择了四个站点来测试和评估新模型。在评估的2年中，太阳活动指数最高。将新模型结果与自适应神经模糊推理系统 (ANFIS) 和人工神经网络 (ANN) 作为其他软计算算法进行了比较。ANFIS 和 ANN 的训练是使用误差反向传播 (BP) 方法完成的。新模型已在不同的日期、月份和季节进行了评估。为了验证结果，使用均方根误差 (RMSE)、相关系数、残差直方图、相对误差和标准偏差。此外，所有结果都与作为传统电离层模型的全球电离层图（GIM）的 TEC 进行了比较。对于 SVM、GIM、ANFIS 和 ANN 模型，测试站的平均 RMSE 分别为 3.642、6.723、4.844 和 5.011 TECU。此外，在测试站评估的四个模型的平均相关系数分别计算为 0.910、0.742、0.863 和 0.841。获得的 SVM 模型的最大和最小标准偏差分别为 4.064 和 3.075 TECU。GIM、ANFIS 和 ANN 模型的标准偏差大于 SVM 的标准偏差。评估表明，SVM 在模拟强烈太阳活动期间电离层 TEC 的时间和空间变化方面具有很高的准确性。此外，在精确点定位 (PPP) 中进行的分析表明，与测试站的其他三个模型相比，新模型的精度更高。结果表明，SVM 模型是传统电离层模型的准确可靠替代方案，用于模拟伊朗地区 TEC 的时空变化。