Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM

Performing an operational safety situation evaluation of the terminal area control system is crucial for enhancing safety management and ensuring operational safety in the terminal area. We use a combination active–passive risk source identification method to thoroughly identify the safety features of the terminal area control system, and then establish a related indicator system. After that, the system’s secondary and primary indicators are used as input and output vectors for an Extreme Learning Machine (ELM). This creates an ISSA-ELM-based risk probability prediction model for the primary indicators, which gives us the predicted values of the primary indicator risks. Bayesian theory merges the output results to determine the overall safety status of the terminal area control system. The evaluation results indicate a distinct correlation between the secondary and primary indicators in a terminal area of North China. The ELM, which has been improved by the Sparrow Search Algorithm (ISSA), correctly displays this mapping and predicts the operational risks. The improved model is able to make predictions that are more than 80% accurate. A comparison of the model’s evaluation results with the actual operational conditions during the same period demonstrates consistency, suggesting that this method may consistently evaluate the operational safety status of the terminal area control system.