# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 5 Experimental Results
### Validation on Typhoon Cases  
According to the weather reports of the China Weather Typhoon Network, in 2015, super typhoons Chan-hom and Soudelor caused huge economic damage, even loss of life in China (typhoon.weather.com.cn, accessed on 14 January 2021). Therefore, in order to better test the actual prediction effect of one of the typhoon intensity forecast models (i.e., LSTM-8), the super typhoons Chan-hom (2015, No. 9) and Soudelor (2015, No. 13) were used to test the model and to compare it with the prediction model based on an FNN. As per the results (Table 10), the prediction model based on LSTM showed the best prediction results, with lower prediction errors even an a lead time as long as 120 h. Moreover, for the performance of the prediction models (Table 11), the mean values of the LSTM model were smaller than those of the FNN model. The LSTM model showed better prediction ability for the typhoon intensity forecasting. Therefore, the typhoon intensity forecasting model based on LSTM is practical, even for the new typhoon cases and not just for the test set. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Typhoon Cases**} & \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Pressure (Lead Time, h)**} & \multicolumn{8}{c}{**Wind Speed (Lead Time, h)**} \\ \cline{3-14}  & & **6** & **12** & **24** & **48** & **72** & **96** & **120** & **6** & **12** & **24** & **48** & **72** & **96** & **120** \\ \hline \multirow{3}{*}{Chan-hom} & FNN & 7.17 & 9.99 & 14.29 & 20.83 & 18.98 & 17.18 & 19.31 & 4.25 & 6.24 & 8.05 & 11.37 & 8.89 & 8.11 & 8.44 \\  & LSTM-8 & 4.04 & 8.21 & 13.02 & 13.73 & 9.99 & 7.54 & 15.47 & 2.59 & 4.97 & 7.86 & 8.46 & 5.86 & 3.79 & 8.94 \\ \cline{1-1}  & FNN & 8.37 & 12.17 & 17.69 & 34.14 & 46.35 & 44.45 & 39.55 & 5.09 & 7.87 & 13.92 & 19.15 & 33 & 45.47 & 43.77 \\ \cline{1-1}  & LSTM-8 & 3.85 & 6.79 & 11.07 & 19.67 & 27.58 & 30.79 & 30.69 & 2.08 & 3.48 & 5.13 & 9.48 & 14.19 & 16.69 & 16.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The MAE mean values of the LSTM model compared to the FNN model. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Typhoon Cases**} & \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Pressure (Lead Time, h)**} & \multicolumn{8}{c}{**Wind Speed (Lead Time, h)**} \\ \cline{3-14}  & & **6** & **12** & **24** & **48** & **72** & **96** & **120** & **6** & **12** & **24** & **48** & **72** & **96** & **120** \\ \hline \multirow{3}{*}{Chan-hom} & FNN & 7.24 & 11.54 & 17.03 & 21.11 & 19.6 & 16.28 & 15.31 & 5.11 & 7.92 & 11.79 & 14 & 13.42 & 12.63 & 9.02 \\  & LSTM-8 & 5.61 & 10.72 & 16.19 & 16.58 & 13.47 & 10.07 & 9.69 & 3.5 & 6.82 & 10.42 & 10.33 & 8.05 & 5.73 & 5.63 \\ \cline{1-1}  & FNN & 11.25 & 16.75 & 24.33 & 43.24 & 52.81 & 51.67 & 47.53 & 7.63 & 11.84 & 22.67 & 27.72 & 40.48 & 50.21 & 49.06 \\ \cline{1-1}  & LSTM-8 & 5.33 & 9.33 & 15.29 & 25.59 & 33.68 & 36.28 & 33.55 & 2.84 & 4.62 & 7.13 & 12.6 & 17.58 & 19.71 & 18.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The RMSE mean values of the LSTM model compared with the FNN model. Figure 7: The prediction curves of the models based on LSTM and the FNN on test sets of 72, 96 and 120 h ahead. To validate the performance of the typhoon intensity model based on LSTM, according to the evaluation indicators MAE and RMSE, the uncertainties of the model are studied. Figure 8 shows the evaluation indicators of the models based on FNN and LSTM using the typhoon case Chan-hom in 2015. Figure 8a shows the MAE of the minimum pressure predicted by the models; Figure 8b shows the RMSE of the minimum pressure predicted by the models; Figure 8c shows the MAE of the maximum wind speed predicted by the models; Figure 8d shows the RMSE of the maximum wind speed predicted by the models. It can be seen that the uncertainties of the model based on an FNN are higher than the model based on LSTM with the same lead time.