# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 6 Conclusions  
When a typhoon arrives, it can bring with it huge losses to the lives and property of people. Improving the prediction results of typhoon intensity, such as accurate and timely typhoon intensity forecasting for as long as 120 h, remains a challenge. As shown by existing studies, suitable prediction factors can improve the prediction results, while more or fewer prediction factors could lead to worse prediction results. Moreover, the prediction model based on an FNN shows a limited ability for typhoon forecasting with a longer lead time of more than 48 h. Therefore, some prediction models based on LSTM are designed to determine the optimal prediction factors. Then, with the optimal prediction factors, one of the models based on LSTM is validated using the typhoon cases Chan-hom and Soudelor in 2015, compared to the FNN model using the same prediction factors. During all of the experiments, the typhoon intensity forecasting was studied as a time series problem based on historical typhoon data and LSTM.  
Figure 10: The wind speed prediction curves of the models based on an FNN and LSTM on typhoon Chan-hom (2015).  
Figure 9: The pressure prediction curves of the models based on an FNN and LSTM on typhoon Chan-hom (2015).  
As per the results, for typhoon intensity forecasting, the suitable and effective prediction factors can easily obtain better prediction results. The prediction factors containing the maximum wind speed and the minimum pressure near the typhoon's center, as well as the latitude and longitude of the typhoon's center at the current time and in the previous 6 h are enough to forecast typhoon intensity with lead times of 6, 12, 24, 48, 72, 96, and 120 h. Compared to the FNN model, the LSTM model shows better prediction ability, has lower uncertainties and smaller prediction errors, and is practical and more stable even for newer typhoon cases. It is very meaningful and practical to study typhoon intensity forecasting as a time series problem, as well as with a long lead time of more than 48 h. The application and findings of this study may be helpful and meaningful for engineering modeling, typhoon forecasting, and research of numerical models. In future work, more experiments and methods will be considered to make the future experiments of typhoon intensity forecasting better in terms of being more timely and accurate.  
[PERSON] wrote the original manuscript, designed the typhoon intensity forecasting models, and performed the experiments and validation; [PERSON] and [PERSON] supported the project and reviewed and edited the manuscript; [PERSON] and [PERSON] provided help and support as experts in the area of atmosphere, including the research of typhoon predictability. All authors read and agreed to the published version of the manuscript. This study was supported by the National Key Research and Development Program of China (Grant Number 2020 YFA0608002; 2018 YFC1506402), the Shanghai 2020 \"Science and Technology Innovation Action Plan\" for Social Development (Grant Number 20 dz1200702), the National Natural Science Foundation of China (Grant Number 42075141), and the Fundamental Research Funds for the Central Universities (Grant Number 22120190207). The authors declare no conflict of interest. The authors declare no conflict of interest.