# Ionospheric TEC Prediction in China Based on the Multiple-Attention LSTM Model
## 1 Introduction  
The ionosphere is an important ionization region of the earth's upper atmosphere, with an altitude of approximately 60-1000 km. The ionosphere is affected by solar storms, geomagnetic activities and other factors, which will generate ionospheric disturbances and have an important impact on radio and other communication signals transmitted in the ionosphere [1, 2]. The total electron content (TEC) is among the important parameters for studying ionospheric changes [3, 4]. Accurate prediction of TEC is very important for communication systems such as satellite positioning and remote sensing systems [5, 6]. With TEC prediction, corresponding protection measures can be started in satellite navigation, satellite positioning, and radio communication in the case of poor space weather conditions. Therefore, it is very important to study the TEC prediction model and predict it as early as possible. There are two main types of ionospheric short-term prediction methods: the first type are the methods combining observation data with the ionospheric theoretical model [7], and the other type is based on the artificial neural network (ANN) [8; 9]. Among them, the artificial neural network has become a popular tool in ionospheric TEC modeling and prediction due to its strong non-linear representation ability [10]. [PERSON] et al. [11] used an ANN model to predict the daily and seasonal effects of TEC on the Indian equatorial station Changanacherry. [PERSON] et al. [12] proposed a neural network model to predict TEC in Thailand. However, TEC data are typical time series data, which contain a strong time correlation. The TEC prediction methods based on the artificial neural network only consider the spatial position of the data and cannot characterize the characteristics of the time series, which will lead to a large prediction error. [PERSON] et al. [13] showed that the ANN model could not reflect the time series characteristics of data, resulting in large prediction errors and low prediction accuracy in different seasons; [PERSON] et al. [14] showed that a RBF neural network is not sensitive to the daily changes of TEC, resulting in a large prediction error at night. [PERSON] et al. [15] showed that the ANN model is susceptible to the interference of solar activity, and the TEC prediction error varies greatly in the high and low years of solar activity. The model is insensitive to the seasonal changes of TEC, resulting in low prediction accuracy. The recurrent neural network (RNN) is a chain-connected neural network that takes sequence data as input and performs recursion in the evolution direction of the sequence. It is a deep learning model that can characterize both the spatial characteristics and the temporal characteristics of the data. It is the mainstream algorithm for time series modeling [16]. [PERSON] et al. [17] showed that the RNN can predict TEC; however, a unit in the RNN is mainly affected by the units near it, which will make it difficult for the gradient to be transferred back from the back layer during long-time series prediction, causing the gradient to disappear, which makes it unable to represent the non-linear relationship of long-time span, that is, it cannot solve the long-term dependence of data. Long Short-Term Memory (LSTM) can solve the gradient disappearance problem by its unique gate structure [18]. [PERSON] et al. [19] used the LSTM model to predict TEC. However, since the LSTM model treats historical time series equally, it cannot adaptively focus on important features, which results in limited prediction accuracy. To solve this problem, this paper adds multiple attention modules and an L1 constraint to the traditional LSTM model. The attention modules can redistribute weights according to the importance of multiple historical data input to the model to improve the attention of the model to important features [20; 21; 22]. L1 constraints can prevent the model from paying too much attention to certain features.