# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 4 Case Study Analysis
We established the initial population size for the ISSA at 30, and fixed the number of iterations at 100. Figures 5-7 depict the outcomes from the testing set. Figures 5-7 show the plots of the probabilistic prediction results for altitude deviations, airborne hazardous approaches, and unstable approaches, respectively. The following are examples of the reasons for the fluctuations in the output results. In Figure 5, at T = 7, as the terminal area control system is at the peak of flight traffic, the controllers are under great work pressure and need to deal with the altitude assignment and monitoring tasks of multiple aircrafts at the same time, their psychological condition is affected, and, under pressure or nervousness, the pilots may also manipulate the altitude system excessively due to psychological fluctuations, coupled with the high pressure of data processing of the radar equipment and the high degree of loading of the equipment in the busy hours. This, coupled with the high pressure on the radar equipment data processing and excessive equipment loading during peak hours, leads to the increased risk of altitude deviation. The drastic weather change is also a secondary indicator that cannot be ignored by the terminal area control system, which is closely related to the increase in various risks. At T = 26, we found that the probability of altitude deviation, airborne hazardous approach, and unstable approach were all higher because of a series of impacts caused by the light rain at that time. The accuracy of the risk probability forecasts for the major indicators of the operational safety situation in the terminal area control system is 81.27%, 85.08%, and 85.22% according to the prediction findings. The 19 secondary safety indicators that were chosen are linked to the three main unsafe events that happened in the terminal area. This shows that the improved Extreme Learning Machine correctly identifies this link, which makes it easier to predict the operational risks in the terminal area control system. We obtained the probability of occurrence of three typical unsafe events when the terminal area control system is at risk based on five years of historical data on unsafe events, and, over the past five years, the probability of an altitude deviation, the probability of an airborne hazardous approach, and the probability of an unstable approach in the event of an unsafe event in the terminal area was 0.18, 0.3, and 0.52. Then, based on Equation (11), we defined the set of dangerous events for the terminal area control system as \(\mathrm{F}=\mathrm{f}_{\mathrm{i}}\). When \(F=f_{\mathrm{i}}\), the probability of operational risk increases. The expression for A equating to Y is \(\mathrm{P}(\mathrm{A}=\mathrm{Y}|\mathrm{F}=\mathrm{f}_{\mathrm{i}})\). We consequently computed the anticipated risk probabilities for the terminal area control system, as shown in Figure 8.  
Based on Equation (12), combined with the results of the risk probability prediction obtained in Figure 8, we calculated the value of the operational safety posture of the terminal area control system. Table 7 presents the derived operating safety status value for the terminal area control system based on the anticipated risk probability. The integer part of the safety posture value obtained from the formula indicates the most serious risk that may exist in the system. It can be found that the most serious risk that may exist in the system has a very low risk, and, due to the fluctuation of the elements, only the absence of risk is desirable, so that all of the values of the safety posture are greater than one. We also find that the operational safety metrics for the terminal area control system show a tendency to stabilize throughout the sampling period. Figure 7: Prediction results of the unstable approach probability. Figure 9 depicts the operational safety condition trend for the terminal area control system.