# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 2 Materials and Methods
### Operational Safety Situation of the Terminal Area Control System  
#### 2.3.1 Prediction of Operational Risk Probability in the Terminal Area Control System  
The ISSA-ELM model yields estimated risk probabilities for the major indicators of the operational safety conditions of the terminal area control system.  
In order to make the security situation assessment results more accurate and to make the model in this paper more applicable to the complex and changing terminal area environment, the Bayesian theorem was used to integrate the predictions so to determine the overall risk probability associated with the operations of the terminal area control system. The dynamic updating capability of Bayesian theory makes it more suitable for dealing with complex and variable security assessment environments than other probabilistic approaches. Some traditional probabilistic models, once parameterized, have difficulty in flexibly adjusting their predictions in the face of new information. In contrast, the Bayesian approach is able to continuously adjust the probability distribution according to new information, which is more in line with the dynamic change in risk in the security assessment. In the process of security situation assessment, Bayesian theory can update the probability estimation of the security risk in real time by taking these new ELM prediction results as new evidence, and this precise update can make the prediction results closer to the real risk state, which will also make the results of the subsequent security situation assessment more accurate.  
Let \(T=\{F,A\}\) be a discrete variable set, where \(F=\{f_{1},f_{2},\ldots,f_{n}\}\) represents the collection of unsafe events within the terminal area control system, corresponding to the primary indicators of the operational safety situation. A denotes the operational risk of the terminal area control system, where \(A=Y\) indicates the occurrence of operational risk. When the primary indicator \(F\) takes the value \(f_{i}\), the probability of \(A=Y\) is expressed as follows:  
\[P(A=Y|F=f_{i})=\frac{P(A=Y)\cdot P(F=f_{i}|A=Y)}{P(F=f_{i})} \tag{11}\]  
According to the Civil Aviation Air Traffic Management Safety Management System Construction Guidance Manual, Appendix 8, the evaluation matrix method qualitatively classifies the likelihood of risk occurrence into the following five levels: frequent, occasional, rare, unlikely, and extremely unlikely, while the severity of the risk consequences are qualitatively classified into the following five levels: catastrophic, hazardous, serious,minor, and negligible, assigning numerical boundaries to each natural language variable, respectively. At the same time, five numerical evaluation calibrations of 5, 4, 3, 2, and 1 are given. Based on this, combined with the practical operation and management, and the experience accumulated by civil aviation in quantitative risk evaluation, this paper takes the five numerical values as the weights of the risk level, divides the numerical boundaries of each language level, and adds a new no-risk level, assigning a weight value of 0 with the advice of experts. Although the no-risk condition is a very ideal state, its inclusion in the risk level can make the classification of the risk level more comprehensive. Table 1 presents the classification.