## 4 Experiment
### Experimental Results
#### 4.4.2 Comparison Experiment with Deep Learning Methods
Overall, the AE-GRU model was appropriate for predicting tropical cyclones' tracks, because the auto-encoder layer in this model could extract implicit features from the tropical cyclone tracks and meteorological variables, which could improve the generalization \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Predicted Time Range** & **Test Avg APE** & **Test Max APE** & **Test Min APE** \\ \hline \multirow{4}{*}{RNN [16]} & 12 h & 164.22 km & **344.24 km** & 1.42 km \\  & 24 h & 197.52 km & 348.67 km & 1.47 km \\  & 48 h & 177.37 km & 348.67 km & 2.74 km \\  & 72 h & 174.97 km & 348.29 km & **0.38 km** \\ \hline \multirow{4}{*}{AE-RNN [44]} & 12 h & 150.36 km & 345.39 km & **0.78 km** \\  & 24 h & 164.04 km & 347.57 km & 2.81 km \\  & 48 h & **173.28 km** & 346.19 km & 2.44 km \\  & 72 h & 174.67 km & 347.46 km & 2.03 km \\ \hline \multirow{4}{*}{GRU [10]} & 12 h & 171.07 km & 347.47 km & 3.25 km \\  & 24 h & 172.84 km & 347.56 km & **0.31 km** \\  & 48 h & 182.04 km & 346.13 km & **1.04 km** \\  & 72 h & 175.82 km & 348.26 km & 2.24 km \\ \hline \multirow{4}{*}{LSTM [24]} & 12 h & 173.79 km & 348.19 km & 4.71 km \\  & 24 h & 173.67 km & 347.95 km & 0.90 km \\ \cline{1-1}  & 48 h & 175.02 km & 342.53 km & 2.36 km \\ \cline{1-1}  & 72 h & 175.75 km & 348.70 km & 1.17 km \\ \hline \multirow{4}{*}{AE-GRU} & 12 h & **138.67 km** & 348.27 km & 2.55 km \\  & 24 h & **143.23 km** & **347.55 km** & 0.98 km \\ \cline{1-1}  & 48 h & 174.57 km & **343.66 km** & 1.75 km \\ \cline{1-1}  & 72 h & **174.62 km** & **344.96 km** & 1.91 km \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results among RNN, AE-RNN, GRU, and AE-GRUcapability and efficiency, and the GRU layer had the ability to deal with time-series prediction and also solved the long time dependency problem in traditional time series models. To further demonstrate the effectiveness of the model in predicting individual tropical cyclones, the strong typhoons Meranti, Haiyan, Tip, and Dujuan were chosen to visualize the predicted tracks. The results were based on the previous 72 h data to forecast the next 72 h. Figure 8a-d shows the prediction tracks of the typhoons Tip, Meranti, Dujuan, and Haiyan, respectively. The blue lines in these figures indicate the predicted track, and the red lines represent the actual tropical cyclone track. Figure 8 shows that our proposed model could achieve a good performance in predicting strong typhoon tracks when using the tropical cyclone location data and the meteorological variable data. The predicted tracks were close to the real tracks. Table 7 shows the average APE, max APE, and min APE between the predicted track and the actual track of the target typhoon. The table indicates that the average APEs of all four typhoons were less than 200 km and the maximum APEs were less than 340 km, among which the typhoons Tip and Dujuan had relatively larger errors than the other two typhoons because the typhoon tracks were affected by other factors as well, including the geostrophic deflection force, the location of the subtropical high pressure zone, the influence of cold air, and the topography of the inland areas, which made the learned features from the typhoon tracks and meteorological variables in the AE model insufficient to represent the typhoon features at the current timestamp.