# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 5 Experimental Results  
### Determining the Optimal Prediction Factors  
Based on the proposed models mentioned in the above sections, the models based on LSTM were trained and tested. It can be seen that LSTM-8 achieved the best results of the minimum pressure forecasting near the typhoon's center, as well as the maximum wind speed near the typhoon's center, compared to the other LSTM models (Table 6). The results show that the intensity and position data of the typhoon at the current time and in the previous 6 h effectively improved the performance of the typhoon intensity forecasting. More or fewer prediction factors would make the prediction results of the typhoon intensity worse. To ensure the optimal prediction factors for typhoon intensity forecasting, the prediction factors in the previous 12 and 18 h were also considered to validate the optimal prediction factors. The prediction models can be seen in Table 7. The LSTM-11 and LSTM-12 models also used the hyperparameters of the LSTM-8 mentioned in Section 4. The rolling forecasting and ensemble forecasting methods were used for the typhoon intensity forecasting based on the LSTM-11 and LSTM-12 models. In other words, these LSTM models have the same structure and hyperparameters, but not the prediction factors. \begin{table}
\begin{tabular}{l 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-1 & 4.02 & 7.14 & 12.24 & 19.65 & 24.18 & 26.23 & 26.04 & 2.26 & 4.11 & 7.17 & 11.71 & 14.43 & 15.53 & 15.3 \\ LSTM-2 & 3.44 & 6.07 & 11 & 18.15 & 22.44 & 24.64 & 25.09 & 1.95 & 3.43 & 6.31 & 10.62 & 13.1 & 14.3 & 14.52 \\ LSTM-3 & 3.85 & 6.46 & 10.59 & 16.45 & 20.4 & 23.02 & 24.17 & 2.16 & 3.61 & 6.01 & 9.44 & 11.65 & 13.03 & 13.79 \\ LSTM-4 & 3.42 & 5.74 & 9.87 & 15.49 & 18.9 & 21.42 & 23.6 & 1.89 & 3.15 & 5.45 & 8.81 & 10.82 & 12.34 & 13.7 \\ LSTM-5 & 4.02 & 7.04 & 11.75 & 18.02 & 21.49 & 22.78 & 22.41 & 2.28 & 4.06 & 6.92 & 10.78 & 12.76 & 13.39 & 13.12 \\ LSTM-6 & 3.35 & 5.73 & 10.09 & 16.1 & 19.6 & 21.23 & 21.38 & 1.9 & 3.3 & 5.9 & 9.56 & 11.5 & 12.31 & 12.38 \\ LSTM-7 & 3.72 & 6.11 & 9.84 & 14.76 & 17.68 & 19.57 & 22.54 & 2.06 & 3.37 & 5.52 & 8.36 & 9.95 & 11.13 & 12.42 \\ 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-9 & 3.79 & 6.27 & 10.1 & 15.19 & 17.94 & 19.28 & 23.74 & 2.09 & 3.46 & 5.67 & 8.71 & 10.34 & 11.27 & 12.49 \\ LSTM-10 & 3.42 & 5.55 & 8.93 & 12.71 & 15.19 & 18.08 & 20.85 & 1.9 & 3.09 & 5.03 & 7.32 & 8.78 & 10.41 & 11.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The MAE mean values of the LSTM models. \begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Function Expression** \\ \hline LSTM-1 & P(i+1), WS(t+i) = f[P(t+i-1), WS(t+i-1)] \\ LSTM-2 & P(t+i), WS(t+i) = f[P(t+i-1), WS(t+i-1), P(t+i-2), WS(t+i-2)] \\ LSTM-3 & P(t+i), WS(t+i), Lat(t+i) = f[P(t+i-1), WS(t+i-1), Lat(t+i-1)] \\ LSTM-4 & P(t+i), WS(t+i), Lat(t+i) = f[P(t+i-1), WS(t+i-1), Lat(t+i-1), P(t+i-2), VS(t+i-2), LAT(t+i-2)] \\ LSTM-5 & P(t+i), WS(t+i), Lon(t+i) = f[P(t+i-1), VS(t+i-1), Lon(t+i-1), P(t+i-2), LAT(t+i-2)] \\ LSTM-6 & P(t+i), WS(t+i), Lon(t+i) = f[P(t+i-1), VS(t+i-1), Lon(t+i-1), P(t+i-2)] \\ LSTM-7 & P(t+i), WS(t+i), Lat(t+i), Lon(t+i) = f[P(t+i-1), VS(t+i-1), Lat(t+i-1), Lon(t+i-1)] \\ LSTM-8 & P(t+i), WS(t+i), Lat(t+i), Lon(t+i) = f[P(t+i-1), VS(t+i-1), Lat(t+i-1), Lon(t+i-1), \\  & P(t+i-2), VS(t+i-2), Lat(t+i-2)] \\ LSTM-9 & P(t+i), WS(t+i), Lat(t+i), Lon(t+i), MS(t+i) = f[P(t+i-1), WS(t+i-1), Lat(t+i-1), \\  & Lon(t+i-1), MS(t+i-1), P(t+i-2), VS(t+i+2), Lat(t+i-2), Lon(t+i-2), MS(t+i-2)] \\ \hline \hline \end{tabular}
\end{table}