# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 1 Introduction
The combination of primary and secondary indicators contributes to the robustness of the model in terms of both condition adaptability and model updating. In terms of condition adaptability, the three probabilities of occurrence of insecurity events as primary indicators can maintain their core assessment value under various scenarios and conditions, providing a relatively stable foundation for the model under different scenarios and enabling the model to maintain its basic predictive ability, while the secondary indicators can be flexibly adjusted to different situations by adjusting data according to specific scenarios and changes in conditions, and the combination of the secondary indicators with the primary indicators are combined so that the model can better adapt to different terminal area operation scenarios and improve the robustness of the model under various complex conditions. In terms of model updating and evolution, the three primary indicators, as the core components of the model, are relatively stable in definition and importance, and will not be changed easily. No matter how the model is updated to incorporate new assessment methods or consider new factors, it is always an unsafe event that measures the operational risk of the terminal area. This stability allows the model to have a fixed core reference point during the updating process and ensures that the model does not lose its original assessment direction. In this system, the risk probabilities of the primary indicators function as output vectors, and the secondary indicators are the ELM input vectors [20]. We use the ISSA to optimize the model's Index Parameter, resulting in the ISSA-ELM primary indicator risk probability prediction model. The terminal area control system ultimately derives the overall operational safety situation assessment by combining the predicted findings of the primary indicator probabilities with Bayesian theory [21; 22; 23]. This serves as a useful guide for safety management decision making. Air traffic control safety situational awareness assessment involves a variety of complex data, which come from different sources; many of the current ATC safety assessment models are based on linear assumptions [8; 9; 10; 11], whereas there are a large number of nonlinear relationships in the actual air traffic system; secondly, ATC safety involves a number of interconnected risk factors, and the current assessment methods may only focus on some of the major factors while ignoring the interactions and synergies between the factors. In addition, the ATC safety situation changes rapidly, which requires the assessment system to be able to update and feedback the safety situation in real time. Therefore, it is necessary to establish the model in this paper. First, dynamic modeling allows for the real-time updating of the model structure and parameters to adapt to dynamic changes in the ATC environment. It can continuously adjust the assessment of the safety situation based on new data. Secondly, the ELM has a strong nonlinear fitting ability, which can effectively deal with nonlinear relationships in ATC safety assessment, and can learn complex functions to describe the relationship between different safety factors. Moreover, the ELM is also characterized by a fast training speed and can process a large amount of data in a short period of time, which is very important for ATC safety assessment with high real-time requirements.