## 1 Introduction
[13], several architectures, single-step self-prediction model, single-step auxiliary prediction model, multistep self-prediction model, and multistep auxiliary prediction (MSAP) model, were used for TEC prediction based on International GNSS Service (IGS) maps with an advance time of 24 h, 48 h, and 144 h. Their algorithms are presented in the appendix of the paper [13]. The database covered a set of TEC values from January 2011 to December 2019 and indices included the following: the sunspot number R, F10.7, ap, and Dst. In contrast to traditional data processing methods, the cycle consisted of 90 days. Among these, the first 30 days are considered as the training set, the middle 30 days are considered as the validation set, and the last 30 days are considered as the test set. Comparing the interval within 24 h, the averaged MAE (RMSE) values calculated from the single-step self-prediction model within 48 h increases from 2.721 (3.806) TECU to 3.259 (4.483) TECU. For the MSAP model, these values were 2.116 (3.033) TECU for 24 h and 2.225 (3.175) TECU for 48 h. As the prediction interval increases, the differences become larger: 5.53 (7.427) TECU and 2.485 (3.511) TECU for MSAP and 144 h.  
A TEC forecasting model based on deep learning was proposed by [PERSON] et al. [14], which consists of a convolutional neural network (CNN), a long short-term memory (LSTM) neural network, and an attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. The dataset included TEC from 24 GNSS stations of China Network over the 9-year period (from 2010 to 2018) to predict the value of the TEC 24 h ahead. The data from 2010 to 2017 is used as the training set and the data from 2018 is used as the test set. Bz, Kp, Dst, and F10.7 indices are used to characterize solar and geomagnetic activity. The results were compared to the accuracy of models such as NeQuick, LSTM, and CNN-LSTM. The statistical results were obtained from the data of 24 GNSS stations, 24 h a day and 365 days a year in the test set. For all stations, the MAE was 2.6 TECU for the NeQuick, 1.53 TECUfor the LSTM, 1.36 TECU for the CNN-LSTM, and 1.17 TECU for the CNN-LSTM-Attention model. The RMSE for the NeQuick was 3.59 TECU, 2.25 TECU for the LSTM, 2.07 TECU for the CNN-LSTM, and 1.87 TECU for the CNN-LSTM-Attention model. The latitudinal and longitudinal dependencies of forecast accuracy were also determined from the data of six stations with similar latitudes and longitudes. RMSE decreases with the increase in latitude near the same longitude, which is about 1 TECU in the mid-latitude region and between 1.6 and 3 TECU in six stations around 30\({}^{\circ}\)E. Accuracy estimates depending on the level of disturbance were performed separately for days with Kp < 3 and Kp \(\geq\) 3. For a quiet day on 21 August 2018, the statistics showed RMSE = 4.14 TECU, MAE = 3.16 TECU, and the mean error ME = \(-\)0.12 TECU for the NeQuick model. For the LSTM model, statistical estimations have provided the following results: RMSE = 4.29 TECU, MAE = 3.0 TECU, and ME = \(-\)1.5 TECU. For the other architecture, CNN-LSTM, the following error values were obtained: RMSE = 4.14 TECU, MAE = 2.98 TECU, and ME = \(-\)1.62 TECU. For the proposed CNN-LSTM-Attention architecture, the results were better: RMSE = 3.99 TECU, MAE = 2.81 TECU, and ME = \(-\)1.46 TECU. During the perturbed day of 26 August 2018, the magnetic storm led to big changes of TEC, and the accuracy of all methods decreased. For the NeQuick model, RMSE = 4.99 TECU, MAE = 3.67 TECU, and ME = 1.71 TECU. The forecast for the LSTM method provided the following values: RMSE = 5.19 TECU, MAE = 3.76 TECU, and ME = \(-\)2.07 TECU. These values decreased to RMSE = 4.43 TECU, MAE = 3.06 TECU, and ME = 0.21 TECU for CNN-LSTM and to RMSE = 4.11 TECU, MAE = 2.81 TECU, and ME = \(-\)0.64 TECU for the CNN-LSTM-Attention architecture. [PERSON] et al.