## 1 Introduction
[4] proposed the ConvRNN method and compared the results with the data presented in the papers [15; 16; 17] for specific Chinese stations and on a global scale, with a lead time of 2 and 48 h. In the range 22\({}^{\circ}\)N-39\({}^{\circ}\)N, an increase in the forecast accuracy with increasing latitude was obtained. On a global scale, the method in [17] provided RMSE = 3.1 TECU and the method in [4] gave 0.89 TECU for a lead time of 48 h and 0.38 TECU for 2 h ahead. [PERSON] and [PERSON] [18] analyzed the data from the equatorial station MAL2 (Kenya) and determined that GRU was more accurate than LSTM, Multilayer Perceptron (MLP), GIM_TEC, and the IRI-Plas 2017. The data covered 9 years from 1 January 2010 to 31 December 2018. The data from the year 2010 to 2016 were used for the training, while data from the year 2017 were used for validation and the data in the year 2018 were used to estimate the performance of the models. The GRU unit showed a prediction error of 2.004 TECU while the LSTM, MLP, GIM_TEC, and IRI-Plas 2017 models showed a prediction error of 2.055 TECU, 2.336 TECU, 5.913, and 16.183 TECU, respectively. [PERSON] et al. [19] proposed a spatiotemporal deep learning architecture, CNN-GRU, combining a convolutional neural network to capture the spatial variability of TEC and a gated recurrent unit for temporal variability modeling. Real TEC measurements were used by determining the slant STEC and its conversion to TEC for six stations in different regions of the globe, with F10.7, solar sunspot number SSN, Kp, ap, and Dst as the input parameters. The TEC values obtained by the proposed method were compared not only with the results of the neural network methods, LSTM [20], BiLSTM [11], and RNN [21], but also with the GIM map CODE and IRI model values. The dataset included data from 2014 to 2018 with the test period being during the second half of 2018. Specific estimates of MAE ranged from 0.7-1.8 TECU for CNN-GRU, 0.9-1.8 TECU for Recurrent Neural Network (RNN), and 0.8-2.2 TECU for LSTM and BiLSTM. Unfortunately, many papers do not provide Mean Absolute Percentage Error (MAPE) values, while a comparison of MAE and RMSE does not always give the full picture. As shown in Table III in [PERSON] et al. [19], different methods can give the best results for different stations. As for the dependence of the results on latitude, they were better for the middle-latitude station Graz than for the high-latitude station Tixi. This paper also compares the processing time in minutes and the number of the required trainable parameters per method. The most efficient was the RNN architecture, which requires 23 min (for 200 training epochs), while the heaviest is the BiLSTM model which requires280 min (for the same 200 epochs). The proposed CNN-GRU architecture requires 67 min for its training. However, other approaches are also possible. Using the advantages of neural networks combined with the Global Ionosphere Map GIM, an empirical model to predict TEC, 24 h in advance at the global scale was developed in the work of [PERSON] et al. (2022). A nonlinear autoregressive neural network with eXternal input NARX was used as a neural network. The Polytechnic University of Catalonia's (UPC) products for single point forecasting were used as GIM. To extend the forecasting at a global scale, a NeQuick2 model adapted to an effective sunspot number R12 eff was used. TEC and Kp data for 11 years (2005-2015) divided into training (70%), validation (15%), and testing (15%) datasets with a time resolution of 2 h were used. Examples for four points and two quiet days (30 May 2016 and 21 August 2016) yielded an RMSE of 1.5 TECU, 0.32 TECU, 2.3 TECU, and 3.0 TECU. Accumulated statistics showed that the RMSE was 3.5 TECU for the period June 2017 to May 2018. For the disturbed period of 7-11 September 2017, the RMSE = 3.8 TECU. The RMSE for six per disturbed periods of different intensities (from 1 to \(-\)142 nT) was in the range 3.4-5.1 TECU and showed no dependence on Dst.