## 2 Related Studies  
To forecast tropical cyclone tracks, the traditional methods are based on numerical, statistical, dynamical, and integrated models in general [11]. A numerical model requires considerable computational power to handle complex dynamical equations [12]. The model needs to generate a grid system to simulate the internal structure of tropical cyclones in real time. However, this model is less time-efficient. Different from a numerical model, a statistical model only needs to calculate the behavior pattern of tropical cyclones from historical materials, which improves the efficiency [13]. However, this model cannot produce an accurate enough result. To improve prediction accuracy, the dynamical model combines the dynamic system with the statistical relationship, so that the model can use large-scale variables as a set of predictors in the typhoon prediction scheme [3]. In addition, the integrated model is a predictive model that combines different models, different physical parameters, and different initial model conditions. This model usually yields better predictive values than a single model [14]. Nevertheless, with the gradual establishment of meteorological satellites, ocean observation stations, and ground observation stations, the observation data system is gradually improved so that more and more data are accumulated. It is still an urgent problem to find a method to improve the efficiency and accuracy of tropical cyclone track prediction in large-scale spatio-temporal data. Deep learning algorithms are becoming more mature these days, and increasing numbers of experts have begun to apply them to improve tropical cyclone prediction [15; 16; 17]. A recurrent neural network (RNN) is a deep learning algorithm that is good at processing and modeling sequence data, which is capable of simulating human memory cells [18]. It has been revealed to be well-suited for prediction based on time-series data [19; 20; 21; 22], which conform to the characteristics of the typhoon and hurricane dataset. For example, [PERSON] et al. [23] proposed a typhoon track prediction model combining an RNN with an attention mechanism. Their experimental results showed that the accuracy of typhoon track prediction can be improved to some extent by using the deep learning method. However, naive RNNs have a long-term dependency problem. This means that the current state of the system may be influenced by the state of the system long ago, but an RNN is unable to learn information at large intervals. Then, [PERSON] and [PERSON] [24] proposed a long short-term memory (LSTM) model to deal with the long-term dependency problem and the exploding and vanishing gradient problem, in which the gated control unit and linear connections are used to represent temporal features of sequential data. [PERSON] et al. [25] trained an LSTM neural network using the typhoon observation data during 1949-2011 in Mainland China and developed a novel typhoon prediction method combining the LSTM with big data and machine learning forecast methods. Finally, the model could predict 6-24 h of typhoon tracks. Moreover, a ConvLSTM-based spatio-temporal model with the time-sequential map technique was proposed by [PERSON] et al. [26], which could be used to track and forecast hurricane trajectories from large-scale climate data. However, because of the multiple gated units in the LSTM structure, there is an increase in the number of parameters and a decrease in the training speed. In order to reduce the number of required training parameters in LSTM, a simpler model, the gated recurrent unit model with only one update gate and one reset gate, was developed. Recently, GRU has been widely applied in many fields [27; 28; 29; 30] owing to its outstanding performance and less training time. [PERSON] et al. [31] proposed a model that added a convolutional structure in a GRU in order to predict cloud movement. In this model, both the input and output were spatio-temporal sequences. Compared to the ConvLSTM model, this model had fewer parameters.