# Typhoon Track Prediction Based on Deep Learning
## 1 Introduction
Statistical-based typhoon track prediction methods are based on mathematical statistics, and they are based on a statistical analysis of a large amount of historical data to find the patterns of weather changes and the relationship between typhoon tracks and forecast factors to obtain linear regression equations and forecast typhoon tracks [7; 8; 9; 10; 11]. However, this type of prediction method relies heavily on historical data, and although the accuracy of the prediction results is reasonable for typhoons with similar paths and the trajectories of other typhoons with similar paths in the past, the prediction results will be more inaccurate for typhoons with more special paths (e.g., circular motion, the existence of inflection points) or when the weather is subject to drastic changes. Statistical dynamics-based typhoon path prediction methods combine the above two methods, and although these methods combine dynamics and statistics to improve the prediction accuracy, they still require large computing power and are costly. Figure 1: Frequency of natural disasters and direct economic losses in Hainan Province, 2014–2019. At the end of the 20 th century, with the rapid development of machine learning, a large number of machine learning algorithms emerged, so many researchers began to try machine learning-based methods for typhoon path prediction. For example, using the K-means algorithm [13], combining the principal component analysis method with stepwise regression [14], constructing a numerical forecasting system based on GRAPS [15], constructing a prediction model based on gradient boosting decision tree [16], etc. Although the accuracy has been improved, there are problems such as difficulty in feature selection, poor portability of classifiers, inability to fully automate, and poor sensitivity to changes in typhoon acceleration and deceleration. In recent years, with the proposal and development of deep learning, many scholars have started to try to predict typhoon trajectories based on deep learning, considering its properties such as nonlinearity, strong feature extraction ability, and its ability to learn temporal and spatial features from datasets and to achieve prediction of data in complex structures. The literature [17] used Artificial Neural Network (ANN) to train typhoon satellite images and demonstrated that deep learning can be used to make predictions of typhoons. The literature [18] combined the CLIPER model and the BP neural network to predict the typhoon path, and its accuracy was improved compared with the CLIPER model. [PERSON] et al. [19] built a typhoon path prediction model based on LSTM (Long Short-Term Memory) and found that its error size is affected by the amount of data set and is more suitable for making short-term predictions of typhoon paths. [PERSON] et al. [20] proposed a method that combines static and dynamic neural network models and treats the variables affecting the typhoon path as a time series and uses a nonlinear autoregressive network with a moving average method to predict the typhoon track, which provides a new idea and method for typhoon path prediction. In the literature [21], a classification and prediction method based on a gated unit recurrent neural network was proposed, and he first classified the typhoon using a dynamic regularization algorithm, and then the typhoon path was predicted using GRU. The prediction results of this method are not bad, but the number of typhoons classified, and the basis of classification are somewhat unknown. The literature [22] proposed a fused deep learning method for typhoon path prediction based on reanalysis information, which fuses past track data and reanalysis atmospheric images to build a neural network model with lower time cost and better real-time performance compared to other existing dynamical prediction models. [PERSON] et al. [23] proposed a deep learning model in a distributed system, based on complex features and learned by task, for implementing a typhoon path prediction model. [PERSON] et al.