# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 5 Experimental Results
### Validation on the Test Set  
As the prediction results of the models based on LSTM (Section 5.1), the optimal variables to forecast typhoon intensity are P, WS, Lat, and Lon. Besides, the optimal prediction factors are P, WS, Lat, and Lon at the current time and in the previous 6 h. Then, the model using the optimal prediction factors (LSTM-8 model) was validated on the test set compared to the FNN model using the same prediction factors. As the results (Table 9), according to the evaluation indicators MAE and RMSE, in general, the mean values of the LSTM model were smaller than those of the FNN model. The LSTM model generally showed better prediction ability for the typhoon intensity forecasting.  
Next, according to the evaluation indicators of MAE and RMSE, the uncertainties of the model were studied to validate the performance of the prediction model based on LSTM. Figure 5a shows the MAE of the minimum pressure predicted by the models; Figure 5b shows the RMSE of the minimum pressure predicted by the models; Figure 5c shows the MAE of the maximum wind speed predicted by the models; Figure 5d shows the RMSE of the maximum wind speed predicted by the models. The model based on FNN showed higher uncertainties than the model based on LSTM with the same lead time. The evaluation indicators of the model based on FNN changed greatly with the longer lead time of more than 48 hours, but the evaluation indicators of the model based on LSTM remained stable.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Function Expression** \\ \hline LSTM-11 & P(t+i), WS(t+i), Lat(t+i), Lon(t+i)=f[P(t+i-1), WS(t+i-1), Lat(t+i-1), Lon(t+i-1), P(t+i-2), \text{WS(t+i-2), Lat(t+i-2), Lon(t+i-2), \text{P(t+i-3), Lat(t+i-3), Lon(t+i-3)}} \\ LSTM-12 & P(t+i), WS(t+i), Lat(t+i), Lon(t+i)=f[P(t+i-1), WS(t+i-1), Lat(t+i-1), Lon(t+i-2), \text{P(t+i-2), \text{P(t+i-2), \text{P(t+i-2), \text{P(t+i-3), \text{P(t+i-3), \text{Lent(t+i-3), \text{P(t+i-4), \text{Lent(t+i-4), \text{Lent(t+i-4)}}}}}}}}},\] \\ \hline \hline \end{tabular}
\end{table}
Table 7: The LSTM models using the prediction factors at the current time and in the previous 6, 12, and 18 h.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Pressure (Lead Time, h)**} & \multicolumn{8}{c}{**Wind Speed (Lead Time, h)**} \\ \cline{2-13}  & **6** & **12** & **24** & **48** & **72** & **96** & **120** & **6** & **12** & **24** & **48** & **72** & **96** & **120** \\ \hline LSTM-8 & 3.33 & 5.38 & 8.67 & 12.46 & 14.78 & 17.35 & 20 & 1.84 & 2.98 & 4.87 & 7.12 & 8.5 & 9.99 & 11.57 \\ LSTM-11 & 3.4 & 5.52 & 9.14 & 14 & 16.63 & 18.48 & 19.64 & 1.96 & 3.16 & 5.32 & 8.14 & 9.53 & 10.47 & 11.14 \\ LSTM-12 & 3.41 & 5.46 & 8.93 & 14.07 & 16.88 & 18.16 & 18.83 & 1.95 & 3.13 & 5.29 & 8.41 & 9.94 & 10.58 & 10.89 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The MAE mean values of the LSTM models.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Indicators**} & \multirow{2}{*}{**Models**} & \multicolumn{8}{c}{**Pressure (Lead Time, h)**} & \multicolumn{8}{c}{**Wind Speed (Lead Time, h)**} \\ \cline{3-13}  & & & **6** & **12** & **24** & **48** & **72** & **96** & **120** & **6** & **12** & **24** & **48** & **72** & **96** & **120** \\ \hline \multirow{3}{*}{MAE} & FNN & 3.45 & 5.67 & 9.17 & 13.35 & 15.41 & 16.61 & 18.29 & 1.96 & 3.19 & 5.17 & 7.69 & 8.94 & 9.79 & 10.85 \\  & LSTM-8 & 3.33 & 5.38 & 8.67 & 12.46 & 14.78 & 17.35 & 20 & 1.84 & 2.98 & 4.87 & 7.12 & 8.5 & 9.99 & 11.57 \\ \cline{1-1}  & FNN & 5.21 & 6.99 & 13.17 & 24.71 & 32.21 & 34.81 & 35.87 & 2.79 & 4.02 & 7.74 & 14.65 & 19.36 & 21.48 & 22.12 \\ \cline{1-1}  & LSTM-8 & 4.84 & 7.86 & 12.32 & 17.62 & 21.57 & 25.65 & 29.47 & 2.5 & 4.15 & 6.65 & 9.76 & 12.1 & 14.45 & 16.62 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The MAE and RMSE mean values of the LSTM model compared to the FNN model.