# DR-Transformer: A Multi-Features Fusion Framework for Tropical Cyclones Intensity Estimation
## 4 Experiment
### Ablation Studies  
To demonstrate the effects of the RI loss, DC loss, and transformer module, we performed the loss separation experiments on the TCIR dataset. For all ablation studies, we used Resnet-18 as our backbone and estimate intensity with \(T=7\). The estimation results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{
\begin{tabular}{c} **Intensity** \\ **Range** \\ \end{tabular} } & \multirow{2}{*}{**Numbers**} & \multicolumn{6}{c}{**Approach**} \\ \cline{3-10}  & & & \multicolumn{2}{c}{**Cross-Entropy**} & \multicolumn{2}{c}{**Npair**} & \multicolumn{2}{c}{**Log-Ratio**} & \multicolumn{2}{c}{**Ours (DC + RI)**} \\ \cline{3-10}  & & & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline H5 & \(\geq\)137 kt & 41 & 9.09 & 10.89 & 7.43 & 9.53 & 7.43 & 8.79 & 5.41 & **6.44** \\ H4 & 113–136 kt & 93 & 7.96 & 10.10 & 10.79 & 13.32 & 7.44 & 9.41 & 6.82 & **9.26** \\ H3 & 96–112 kt & 130 & 12.22 & 15.18 & 15.13 & 18.63 & 11.98 & 15.36 & 9.87 & **12.35** \\ H2 & 83–95 kt & 243 & 11.98 & 15.71 & 13.61 & 16.82 & 10.83 & 13.66 & 10.33 & **12.86** \\ H1 & 64–82 kt & 468 & 11.35 & 14.05 & 11.34 & 14.15 & 10.56 & 13.08 & 10.19 & **12.40** \\ TS & 34–63 kt & 1735 & 7.84 & 10.01 & 8.15 & 10.34 & 7.76 & 9.95 & 6.35 & **8.20** \\ TD & 20–33 kt & 1501 & 5.01 & 7.44 & 5.50 & 7.25 & 6.78 & 8.43 & 5.43 & **7.03** \\ NC & \textless{}20 kt & 89 & 8.69 & 9.39 & 5.76 & 7.23 & 3.75 & **6.54** & 6.70 & 8.09 \\ Total & - & 4300 & 7.63 & 10.36 & 8.09 & 10.75 & 7.90 & 10.21 & 6.79 & **8.88** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tropical cyclone estimation results compared with other methods. Bold numbers denote the best results. We divided tropical cyclones into eight categories by SSHWS based on their intensity and we counted the number of each category in the table. Additionally, we used MAE and RMSE as the evaluation metrics.  
Figure 4: From (**a–d**) are the scatter of Npair, CE, log-ratio, and ours (DC + RI) respectively. The horizontal axis represents the groundtruth and the vertical axis represents predicted intensity. The blue line from bottom left to top right represents the best prediction. The grey, orange, and red crosses in the figure represent the errors within 10 knots, between 10 and 20 knots, and greater than 20 knots, respectively. In (**c**), the prediction distribution is very sparse due to the imbalance of categories.  
DC loss: We compare our DC loss with the cross entropy loss. The first row (a) means we only used Resnet18 with cross-entropy loss to supervise learning. We further used a softmax classifier to estimate intensity. Row (b) means we used DC loss to train our extractor and utilize the nearest neighbor classifier to estimate intensity, since our DC loss enhances the classification ability of similar samples and reduces the error caused by misclassification by constrained ratios. By introducing DC loss, the RMSE improved from 10.36 to 9.73 and the MAE improved from 7.63 to 7.44. MAE is reduced to a lesser extent because RMSE is more sensitive to error.  
RI loss: Our RI loss is to suppress the error caused by different shooting angles. Row (c) means we further utilize RI loss and feature prototype. It improves RMSE from 9.73 to 8.81 and MAE from 7.44 to 6.79.  
Transformer: The learning of temporal correlation by transformer leads to large reductions in MAE and RMSE. We conducted two sets of comparative experiments. Row (e) is our pipeline. We utilized the transformer based on the DR-extractor. Compared to row (c), the reduction in RMSE from 8.88 to 7.76 and MAE from 6.79 to 6.01 after applying the transformer. Row (d) means we simply use five-point average weights instead of the transformer to aggregate temporal information. It leads to increases in both MAE and RMSE compared to (e), which shows the transformer can make better use of temporal information.