# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 4 Experimental Setup and Details
### Situation Assessment of Air Traffic Based on Ensemble Learning  
The experiments can prove that the ensemble learning model is the better choice to build the air traffic situation assessment model. Therefore, we input the test dataset into the air traffic situation assessment model. In Figure 6, the distances from 90 samples to the classified hyperplane was used to draw the change diagram of the air traffic situation in East China. And the quantitative assessment results of the air traffic situation assessment model were compared with the actual operation state. The distances between the test samples and the classification hyperplane were output by _decision_function ()_. The dotted red line divides the samples into positive and negative categories.  
Within the positive region, the air traffic situation in East China is at a low risk level. Within the negative category, the air traffic situation in East China is at a higher risk level. According to the color distribution of the samples, the air traffic situation level is \"safety\" or \"potential risk\" on most days. For about 35 days, the air traffic situation level in East China was between \"potential risk\" and \"general risk\". The 14-day situation level is in the negative category, belonging to the \"moderate risk\" and \"significant risk\". The air traffic situation of these samples has been confirmed to have great risks. The distance from the sample to the classification boundary can be used to further determine the risk severity level on that day. In addition, it can be found that there are more samples marked in blue in the positive area. This indicates that the air traffic situation in East China is generally in a relatively safe state within 90 days. And the air traffic operation is relatively stable. In order to analyze the changing trend in the air traffic situation in East China, we draw the  
Figure 5: The PR curve of the ensemble model.  
Figure 6: The distances between the test samples and the classification hyperplane.  
needle graph. The distance from the sample to the classification hyperplane was displayed more intuitively. We can clearly see the fluctuation of the air traffic situation in 90 days.  
In Figure 7, the 90-day air traffic situation changes periodically. Every 10-15 days, the air traffic situation appears to exhibit more intensive fluctuations. As shown in the red circle, the air traffic situation will be affected on the days when significant risk appeared. We acquired the operation of the air traffic in East China on the days corresponding to the No. 5, No. 24, and No. 80 samples. We found that there were a lot of military flights and heavy weather in East China on these days. Some airports and routes were temporarily closed. And flight delays were severe. This led to different levels of flight congestion on the following days in the air traffic. In addition, we also acquired the air traffic operation state of some general and potential risk samples. We have found that minor delays due to routes changes, heavy weather, and traffic control are common on these days. With the influence factors gradually eliminated, the air traffic operation state gradually returned to stability and safety within a few days.