## 4 Case Study
### Parameter Selection  
To better test the prediction accuracy and stability of the WACNN model, several models commonly used in regression prediction problems were selected. The CNN, LSTM, BiLSTM, and GRU models for deep learning, and the RF and SVR models for machine learning were used to make a regression prediction of the container throughput of sea-rail intermodal transportation at Yingkou port and compare them at the same time. The structure of the LSTM and BiLSTM models is shown in Figure 9.  
\(-\)Dividing the data into a training set and a test set, using a portion of the data as the training set for training the model, and then comparing the results with those using the remaining portion of the data as the test set, the results predicted by the trained model were used to assess the accuracy of the model's predictions. To be able to determine better parameters for training the model, several typical values were set as alternative values for each parameter, by referring to the parameter tuning methods in the existing literature. The number of convolutional layers in the CNN model was set as 1, 2, 3, 4, 5, and 6; the number of convolutional kernels per layer were selected as 16-16-16-32-64, 16-16-32-32-64, 16-32-32-64, 16-32-64-64-64, and 16-32-64-64-128; and the dropout was selected as 0, 0.1, 0.2, and 0.3. Through numerical experiments, the effects of the model on the RMSE and MAE of the validation set under different parameter configurations were compared, and the values of each parameter corresponding to the optimization of the evaluation index were selected, as shown in Figure 10.  
Figure 9: Model structure. (**a**) LSTM model structure and (**b**) BiLSTM model structure. Because an increase in the number of layers can lead to overfitting of the model, only a single LSTM layer model and double LSTM layer model were created in the LSTM model. The number of neurons was set to 10, 20, 30, 40, 50, 60, and 70 in the single LSTM; the first layer was set to 10, 20, and 30; and the second layer was set to 10, 20, 30, 40, 50, 60, and 70 in the double LSTM layer. Numerical experiments were conducted to compare the effects of the model on the RMSE and MAE of the validation set under different parameter configurations, and the values of each parameter corresponding to the optimum of the evaluation indexes were selected, as shown in Figure 11.  
In the BiLSTM model, the neurons were set as 10, 20, 30, 40, and 50. The GRU model was set up with two network structures, a single layer and a double layer, and the parameters were set as 16, 32, 64, 128, 16-32, and 32-32, respectively. The comparison of the results is shown in Figures 12 and 13. In the RF, the number of decision trees was set to 50,  
In the SVR, the penalty factor was set to 6, 10, 20, 100, 200, and 500, and the parameters of the radial basis function were 0.2, 0.5, 0.8, and 1.0. Through numerical experiments, we compared the effects of the model on the RMSE and MAE of the validation set under different parameter configurations and selected the values of each parameter corresponding to the optimum of the evaluation indexes, as shown in Figure 15.  
Figure 14: RF model parameter tuning diagram. (**a**) Number of decision trees and (**b**) minimum number of leaves. Figure 12: BiLSTM parameter tuning diagram. Figure 13: GRU parameter tuning diagram. In summary, the overall architecture and parameter settings of the CNN model in the experiment are shown in Table 4.  
As shown in Figure 7, the use of 40 neurons in the single layer model, and 20-30 and 30-40 neurons in the double layer model produced good results. However, it was found in the experiment that if the number of LSTM layers was less, it would result in the model not being able to fully learn the features of the data; so, from an overall consideration, 30-40 neurons in the two-layer model were selected as the optimal parameter here. The overall frameworks and parameter settings of the LSTM, BiLSTM, and GRU models are shown in Table 5.