# An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality
## 4 Case Study
### Sensitivity Analysis of Model Parameters  
The parameters involved in the causality mining method include deep time-domain convolution prediction model parameters and causality identification parameters. The parameters of the deep temporal convolution prediction model include the learning rate, the number of training times, the number of convolution layers, and the size of the convolution kernel. The causality identification parameter includes the value of the candidate causality identification. For the parameters of the deep time-domain convolution prediction model, the learning rate and the number of training times have little effect on the performance of the model and are set with general values. In this model, the learning rate is set to 0.01, and the number of training times is 500. The number of convolution layers and the size of the convolution kernel will have a great impact on the performance of the model, which depends on the size of the data, and they are the two most important parameters which are set through experiments. Considering the size of the experimental data, if the number of convolution layers exceeds 6 and the convolution kernel exceeds 8, the model will be overfitted. To this end, the number of convolutional layers is selected from the set {1, 2,..., 6}, and the size of the convolution kernel is selected from the set {2,..., 7, 8}. The performance of the models constructed with different parameter combinations is analyzed, and the parameter combination with the best model performance is selected from them. In this section, MSE error is used to measure the impact of different convolution layers and convolution kernel sizes on model performance. Experiments show that when the number of convolution layers is 6 and the size of the convolution kernel is 6, the model performance is optimal. After the optimal combination of the number of convolution layers and the size of the convolution kernel is determined, the sensitivity analysis is carried out. One of the parameters is unchanged, and the influence of the other parameters on the model error is analyzed. Figure 11 is a graph of model error versus kernel size and convolution layers. As the number of convolutional layers increases and the convolution kernel becomes larger, the error becomes smaller and smaller. In Figure 11a, when the number of convolutional layers is fixed at 6 and the convolution kernel size is 2, the model prediction error is 9. The reason for the large error is that the convolution kernel is too small, so the receptive field of the input layer is too small, and there is insufficient ability to predict the target airport delay time series. When the size of the convolution kernel is 6, the model prediction error is 2. If the convolution kernel continues to increase, the rate of error decline becomes lower. In addition, if the convolution kernel is too large, it easily causes the model to be too complex and overfit and increases the operation time. In Figure 11b, when the size of the convolution kernel is 6, the effect of the change in the number of convolutional layers on the performance of the model is analyzed. When the number of convolutional layers is 1, the error is 9, and the neural network is too simple, resulting in insufficient abstraction for many input time series, and it cannot predict the target time series well. When the number of convolutional layers is 6, the error is reduced to 2, the model has sufficient ability to represent the input time series information, and the model performance is optimal. The causality identification parameters include the parameter. The parameter is related to the number of candidate causal pairs identified based on the attention score. Too many causal pairs will make the delay propagation causal network too complex, which is not conducive to airport coordination and decision-making. If the number of causal relationship pairs is too small, some important delay propagation causal relationships will be ignored, and delay propagation cannot be reduced to a large extent.