## 4 Experiment
### Experimental Results
#### 4.4.2 Comparison Experiment with Deep Learning Methods  
Comparing with traditional deep learning methods, the performances metrics of RNN, AE-RNN, GRU, LSTM and AE-GRU models are shown in Table 5, which includes the average absolute position error, maximum absolute position error, and minimum absolute position error using the test set to forecast a tropical cyclone position at the next 12, 24, 48, and 72 h. The results were predicted using the previous 72 h locations of the tropical cyclones and the meteorological variables, which meant the time step was 12. For each predicted time range, the bold font is used to emphasize the best prediction results. From Table 5, it is evident that the AE-GRU model performed best, the average APE of which in the 12, 24, and 72 h forecasts were the lowest. That is, the predicted locations were closest to the real locations. Moreover, the 12, 24, 48, and 72 h forecasts demonstrated a decreasing performance because the prediction errors were cumulative over time. The max APE of the AE-GRU model was \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **12 h** & **24 h** & **48 h** & **72 h** \\ \hline A & - & \(>\) 200 km & \(>\)400 km & \(>\)600 km \\ B & 102 km & 215 km & 463 km & 741 km \\ C & 100 km & 170 km & 300 km & 400 km \\ D & 138.67 km & 143.14 km & 174.57 km & 174.62 km \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Twelve hour, 24 h, 48 h, and 72 h forecast results of AE-GRU and Methods A, B, and C.  
Figure 6: Comparison results of NWP and AE-GRU models from 1992 to 2002. Figure 7: Number of effective tropical cyclones and records per year from 1992 to 2002.  
lower than that of the other models for the 24, 48, and 72 h forecasts, with the exception of the RNN, which performed better in the 12 h forecast. This result occurred because the learned information in the RNN-based model could not remember previous information of a long time ago with the increase of time intervals, which made it perform worse in terms of a long prediction time. However, the LSTM- and GRU-based models had memory cells that improved the long-term dependency problem. The best performance of min APE for the 24 and 48 h forecasts was achieved by GRU; however, the largest value of min APE for all five methods was no more than 5 km. In addition, comparing GRU with LSTM, the values of max APE and min APE of the GRU model were lower than the LSTM model. The average APE of GRU performed slightly better than LSTM in the 12 and 24 h forecast. Finally, comparing the AE-RNN and AE-GRU models with the GRU and RNN models alone, the models with an AE layer could achieve a better performance. Therefore, it was necessary to add an AE network to extract deep joint features between the locations and meteorological variables. On the other hand, to better compare the experimental results of the RNN, AE-RNN, GRU, LSTM, and AE-GRU models, the performance metrics' values, including MAE, RMSE, and MAPE, are shown in Table 6. The experiment was implemented to predict tropical cyclone locations at the next 12 h. The best testing results are emphasized in bold font as well. As shown in Table 6, our proposed model performed the best, where almost all performance errors of the testing set were the lowest values. When comparing RNN, LSTM, and GRU models, we could see that both the LSTM and GRU models outperformed the naive RNN model, which indicated that the naive RNN model was inadequate to deal with long-term dependency tropical cyclone tracks. In addition, to compare the GRU model with the LSTM model, we could find that the GRU was more suitable than the LSTM to solve the tropical cyclone track prediction in this study. At last, comparing our proposed model with the GRU-alone model, the performance metrics of our proposed model were slightly better than the GRU-alone model in general. This indicated that all the GRU-based models could achieve good results in predicting tropical cyclones' tracks. However, our proposed model adding an AE layer to the model could achieve a better generalization capability.