# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map  
[PERSON]  
1 School of Microelectronics, Tianjin University, Tianjin 300072, China 1,23 Qingdao Institute for Ocean Technology, Tianjin University, Tianjin 300072, China 1,23 Qingdao Institute for Ocean Technology, Tianjin University, Tianjin 300072, China 1,23 Qingdao Institute for Ocean Technology, Tianjin University, Qingdao 266200, China 3, [PERSON]  
the influence of the ionosphere on the propagation of radio wave signals but also able to summarize the variation laws of the ionosphere at different time and space scales. Furthermore, we will achieve advanced prediction and real-time processing of ionospheric disturbances and reduce the attenuation and variation in the signal propagation process. Considering the importance of accurately reflecting the distribution of electron content in the ionosphere for improving applications such as satellite communication, navigation, positioning [6], measurement, and disaster prevention [7; 8], TEC observation, research, and modeling have always been a hot topic of the exploration for decades. TEC can be directly measured by the ionospheric sounder or indirectly inverted by carrier phase delay during the transmission of the radio signal [9]. In the absence of measurement conditions or other extreme cases, TEC can be calculated by physical models [10; 11], mathematical models [12; 13], and empirical models [14; 15]. Among them, the international reference ionosphere (IRI) is the most common empirical model and is also a well-known recommended international standard, which is often used as a comparison model to verify other models [16; 17]. The IRI model has been steadily improved with newer data and better modeling techniques by the joint working group of the Committee on Space Research and the International Union of Radio Science (URSI) [18]. For some ionospheric parameters such as TEC and the critical frequency of the F2 layer (foF2), there are two sub-models with the corresponding coefficients provided by the URSI and the Consultative Committee of International Radio (CCIR). The latest version is IRI-2016 [18], which determines the electron density by extending the E and F-layer's anchor points of peak electron densities. In particular, the maximum electron density is calculated by the NeQuick model [19]. The recent research has the following properties in modeling TEC. On the one hand, machine learning (ML) and deep learning (DL) methods have been introduced into the modeling of ionospheric parameters and played a good role in improving the accuracy of empirical models [20]. For example, [PERSON] et al. [21] propose a recurrent neural network (RNN) model based on deep learning to predict TEC. Some ionospheric TEC prediction models based on long short-term memory (LSTM) were proposed to predict TEC [22; 23], and some ionospheric TEC maps have been proposed based on bidirectional LSTM algorithms in different regions [24; 25]. A TEC prediction model based on a multi-step auxiliary LSTM algorithm was established and can effectively alleviate the increasing error with prediction time [26]. The TEC forecasting methods based on generative adversarial networks (GAN) [27; 28] and conditional GAN [29] were proposed and improved the IRI [30]. At the same time, [PERSON] et al. have modeled ionospheric TEC by four different ML methods such as artificial neural network (ANN), LSTM networks, adaptive neuro-fuzzy inference system, and gradient boosting decision tree (GBDT) [31]. In addition, the nearest neighbor method [32], prophet model [33], and decision tree [34] based on ML are also introduced into the modeling of ionospheric TEC, providing new modeling ideas and excellent prediction results. On the other hand, compared with the global model, the regional model has made great progress and provided higher prediction accuracy. Therefore, to achieve higher accuracy prediction of specific regions, TEC prediction models focusing on local regions have gradually become a research hotspot in recent years.