# Typhoon Track Prediction Based on Deep Learning
## 4 Conclusions  
In this paper, typhoons originating in the South China Sea are studied for the typhoon track prediction problem. In this paper, a C-LSTM typhoon path prediction model is constructed to form a dataset using typhoon paths and related meteorological variables formed in the South China Sea from 1949 to 2021, and a Granger causality test is used to select multiple features for the dataset before inputting it into the prediction model to realize data dimensionality reduction. In addition, a prediction model based on the LSTM network was constructed for comparison experiments. The final experimental results show that the C-LSTM-based typhoon path prediction model outperforms the LSTM-based typhoon path prediction model, and it works best when the cross-entropy loss function and Adam optimizer are selected, and its average surface distance error is only about 14.66 km.  
Although the feasibility of the C-LSTM-based typhoon path prediction model proposed in this paper is proved through experiments, it still has some limitations and can be optimized further. First, since the causes and triggering mechanisms of typhoons are still unclear and the factors affecting typhoon paths are uncertain, the relationship between typhoon activity and meteorological changes can be further explored in the follow-up. Second, only the South China Sea typhoon data in the best path data set of the Northwest Pacific typhoon were used in this paper, and the influence of typhoon intensity decay on typhoon path and disappearance time was not considered. In the future, we can consider using other existing public data, and we can also consider combining image data and numerical data, or even using the data set obtained by fusing multiple observation methods for typhoon path prediction. That is, the effectiveness and accuracy of forecasts can be improved by increasing the diversity of data and the amount of data used for forecasting. Third, this paper sets up fewer comparative experiments and constructs only one model based on C-LSTM, after which we can try to construct deeper neural network models or build models for typhoon path prediction by combining several other neural networks.  
[PERSON] wrote the manuscript; [PERSON] and [PERSON] collected the data; [PERSON] and [PERSON] edited and revised the manuscript; [PERSON] drew the figures; [PERSON] and [PERSON] designed research methods; [PERSON] analyzed the data; [PERSON] project administration; [PERSON] and [PERSON] funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the High-level Talent Project of Hainan Provincial Natural Science Foundation (620 RC557); Hainan Provincial Natural Science Foundation Innovation Research  
Figure 7: Comparison of the predicted path of Typhoon “Lionrock” using two models, where the blue path indicates the actual path of the typhoon and the orange path indicates the path predicted by different models. (**a**) The comparison of the true path and predicted path using the LSTM model for prediction, (**b**) the comparison of the true path and predicted path using the C-LSTM model for prediction. p.s. The left side of the ocean represents Vietnam, where the non-English text indicates different cities in Vietnam; the right side of the ocean represents Hainan, China, where the non-English text indicates different cities in China.  
Team Project (620 CXTD434); National Natural Science Foundation of China and Macau Science and Technology Development Joint Fund (61961160706 and 0066/2019/AFJ); the Hainan Provincial Key R & D Plan (ZDYF2021 GXJS199). Data are contained within the article. The data presented in this study can be requested from the authors.  
The authors declare no conflict of interest.