## 1 Introduction  
The study of the effects of space weather on the environment is one of the prioritized areas of geophysics [1]. One of the components of the environment is the near outer space of the Earth. Among the parts of this space is the ionosphere, which affects the operation of technological systems such as global navigation satellite systems (GNSS), satellite communications, and other space communications applications [2]. The state of the ionosphere is described by such parameters as critical frequency foF2, maximum height hmF2 of the layer F2, total electron content TEC, and others. The study and prediction of TEC attracts special attention, since its values determine the accuracy of positioning [3, 4]. Currently, among the methods of forecasting the ionospheric parameters, methods using neural networks are distinguished as one of the most diverse and undergoing active development [5]. However, since each region of the globe has different properties related to the influence of space weather, it is necessary to choose a method that can provide the most accurate TEC forecast for each region. This paper attempts to select from a variety of methods the most appropriate one for the European region. For this purpose, both the conventional methods', long short-term memory LSTM [6], gated recurrent unit GRU [7], and new ones including bidirectional [8] and temporal convolutional networks TCN [9], architectures are used. From the extensive literature, we selected those papers that include quantitative estimates of forecast accuracy in different regions. An example of using the LSTM architecture for TEC prediction is a paper which proposed a method with an advance time of 24 h [10]. The authors used CODE (Center for Orbit Determination in Europe) map data with a 1 h step for Beijing station, as well as an index solar radio flux at 10.7 cm (F10.7) of solar activity and geomagnetic activity ap as input parameters. The dataset consists of 1 January 1999 to 1 September 2016. The root mean square error (RMSE) was 3.5 TECU. It is interesting to note that the test years were 2001 and 2015, i.e., years of high solar activity. [PERSON] et al. [11] developed and applied the bidirectional long-term memory (BiLSTM) model to TEC prediction on the same database and using F10.7 and ap indices as in [10]. The results obtained were compared with other methods. The RMSE for BiLSTM was 3.35 TECU; 3.48 TECU was obtained for single-layer LSTM and 3.67 TECU was obtained for double-layer LSTM. A feature that improved the LSTM result is the ability to utilize both the past and future data to make prediction. [PERSON] et al. [12] applied the BiLSTM algorithm to one-hour-ahead forecast the Indian regional TEC map. The training data included 261 days (from November 2015 to September 2016). The validation dataset included 24 days in September 2016, and the testing data were 31 days on October 20. The maps were plotted against data from 26 Global Positioning System stations and compared with Artificial Neural Network (ANN) and LSTM model results during both geomagnetic quiet (20 October 2016) and disturbed (14 October 2016) periods. For quiet conditions, the difference between the experimental and predicted values was from 1 TECU up 2.5 TECU for BiLSTM, and from 1 TECU to 3.5 TECU for disturbed conditions and were better than the results for ANN and LSTM. The LSTM model performed better than the ANN model. A comparison between the Indian regional ionospheric forecast of TEC maps with and without solar and geomagnetic indices (F10.7, Kp) as input to BiLSTM during quiet and disturbed periods showed little improvement. The LSTM architecture has become the reference model against which the results of new approaches are compared, and most often, the results of this comparison are given when they are better than for LSTM. However, the LSTM method continues to be modified. In [PERSON] et al.