# Typhoon Track Prediction Based on Deep Learning
## 1 Introduction  
A typhoon is a natural phenomenon with functions, such as maintaining heat balance and regulating the heat distribution of the earth, but it is also a catastrophic weather system with super destructive power [1], which causes large or small economic losses or even human casualties to the global typhoon origin sea and its coastal areas every year [2]. With a long coastline and in the typhoon-prone northwest Pacific region, China is one of the countries in the world most affected by typhoons. Hainan, located in the South China Sea, is one of the sources of typhoons in the northwest Pacific Ocean, and the influence of the tropical monsoon, known as the \"typhoon corridor\", every year in the summer and autumn, typhoons in the coastal areas will be landed, and transport to and from the island to bring some impact and even cause economic losses and casualties. In addition, in recent years, due to global climate change, typhoons triggered by heavy rains, storm surges, and other natural disasters have shown an increasing trend in frequency and intensity, which means that typhoons in the coastal areas of Hainan people's lives and property security threat are greater. As can be seen from Figure 1, during the period 2014-2019, typhoons ranked among the top three in frequency among all natural disasters in Hainan and were the natural disasters that caused the highest direct economic losses. According to statistics, the super typhoon \"Rammasun\" that landed in Hainan in 2014 caused direct economic losses of 119.5226 billion RMB and affected 3.25829 million people, which is a typhoon that caused the largest direct economic losses in Hainan Province since its founding of China [3]. Strengthening and accelerating the development of typhoon prediction technology is one of the effective means that can reduce the impact brought by typhoons. By predicting the path of typhoons and obtaining their ripple places in advance, disaster prevention departments can provide more accurate information support so that they can have a longer period to develop targeted preventive measures, thus, reducing the casualties caused byt typhoons and property losses to the country and the people. That is, it is of great practical significance to strengthen and accelerate the development of typhoon prediction technology. It is well known that countries have not stopped exploring typhoons. At present, the prediction of typhoon paths can be divided into three categories: Traditional prediction methods, machine learning-based typhoon path prediction methods, and deep learning-based typhoon path prediction methods. Traditional forecasting methods can be divided into two categories, one is the empirical method, which combines information obtained from real-time weather maps with historical similar weather conditions with human subjective experience to forecast the typhoon path. The other is the objective method, which is based on statistics, hydrodynamics and other science to forecast typhoons, and can be subdivided into three types of methods: Dynamics-based typhoon path forecasting, statistics-based typhoon path forecasting and statistical dynamics-based typhoon path forecasting. Dynamics-based typhoon path prediction methods can also be called numerical prediction methods, which are based on a large amount of historical data to construct the atmospheric dynamics equations and transform the typhoon prediction problem into a mathematical problem [4; 5; 6]. However, the equations constructed by such methods are complex and computationally intensive to solve, which will consume a lot of human and material resources, costly, and take a long time. At the same time, such methods contain some empirical and hypothetical relationships, so the possibility of errors in the prediction results is very high.