## 4 Case Study
### Experimental Results  
We input the data into the WACNN, CNN, LSTM, BiLSTM, GRU, RF, SVR, and PLS models with the pre-set parameters. In the models used, the PLS serves as the baseline to \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparametric** & **Selected Hyperparametric Range** \\ \hline Batch size & 16/32/64/128 \\ Initial learning rate & 0.005/0.01/0.02 \\ Learning rate descent factor & 0.2/0.1/0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Sensitivity analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hyperparametric** & **WACNN** & **CNN** & **LSTM** \\ \hline Gradient descent algorithm & SGDM & SGDM & Adam \\ Normalized interval & [0, 1.5] and [0, 1] & [0, 1] & [0, 1] \\ Batch size & 32 & 32 & \\ Maximum number of training sessions & 1500 & 1500 & 1500 \\ Initial learning rate & 0.01 & 0.01 & 0.01 \\ Learning rate policy & Piecewise & Piecewise & Piecewise \\ Learning rate descent factor & 0.1 & 0.1 & 0.1 \\ Learning rate drop rounds & 1200 & 1200 & 1200 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The hyperparameter settings for the WACNN, CNN, and LSTM models. Figure 17: Sensitivity analysis results. validate the predictive performance of the various DL and ML methods. The comparison between the predicted values and the true values of each model on the test set is visualized in Figure 18.  
Figure 18: _Cont._  
A comparison of the prediction results of the seven models for the training and test sets is shown in Table 8.  
Figure 18: Test set results. (**a**) WACNN model test set prediction results; (**b**) CNN model test set prediction results; (**c**) LSTM model test set prediction results; (**d**) BiLSTM model test set prediction results; (**e**) GRU model test set prediction results; (**f**) RF model test set prediction results; (**g**) SVR model test set prediction results; and (**h**) PLS model test set prediction results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Training Set RMSE** & **Test Set RMSE** & **Difference** & **Difference Percentage** \\ \hline WACNN & 13.228 & 15.596 & 2.368 & 17.9\% \\ CNN & 14.7255 & 16.3521 & 1.6266 & 11.05\% \\ LSTM & 68.7971 & 69.3158 & 0.5187 & 0.75\% \\ BiLSTM & 65.0653 & 71.7462 & 6.6809 & 10.27\% \\ GRU & 56.0796 & 61.0531 & 4.9735 & 8.87\% \\ RF & 19.4782 & 29.9312 & 10.453 & 53.67\% \\ SVR & 28.3884 & 28.6886 & 0.5384 & 1.04\% \\ PLS & 289.5629 & 286.2715 & 3.2914 & 1.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of model training set and test set prediction results. From Table 8, the CNN and WACNN performed best on the test set, with RMSEs of 15.596 and 16.3521, respectively, and the WACNN outperformed the CNN, but its training and testing errors were 17.9% and 11.05%, respectively, indicating a certain degree of overfitting. The RF's overfitting was serious, with a difference of 53.67%, and its generalization ability was poor, whereas the SVR, although stable, with a difference of only 1.04%, had a higher prediction error with an RMSE of 28.6886. The time series models, such as the LSTM, BiLSTM, and GRU, performed worse, with a higher RMSE. The PLS performed the worst. In order to visualize the predicted values against the true values, we exported the predictions of the seven models and compared them with the true values. As a result of the large amount of data, we zoomed in on the last 100 days of data. The comparison between the predicted and real values of the seven models is shown in Figure 19.  
The figure shows the prediction results of the various models for container throughput compared with the real value. Among them, the WACNN model learned the overall trend of the data very well, and the overall trend of the CNN model was more fitting, but with some fluctuations. Among the three time series models, the LSTM, BiLSTM, and GRU, the BiLSTM model had less fluctuations in relation to the data, and the LSTM and GRU models had more ups and downs and the trend was not very fitting.