# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 2 Materials and Methods
### Construction of the Operational Safety Situation Indicator System
Radio interference may cause the distortion or interruption of communication signals between the aircraft and ground control. In airspace environments where flights are dense and air traffic is highly congested, significantly increasing the probability of hazardous approaches between aircrafts in the air, adverse weather conditions such as strong winds, turbulence, and low visibility can have a significant impact on the flight performance and maneuverability of an aircraft, which can lead to altitude excursions. In addition, weather factors such as strong winds, rainfall, and snow can interfere with an aircraft's aerodynamic performance, making it difficult for an aircraft during approach to maintain a stable speed, heading, and descent rate. Bird strikes during the approach phase will also cause great damage to the aircraft's approach stability. As the terminal area control system includes multiple airports, an imperfect system of control agreements and irregularities in the transfer of height information during the handover process can increase the likelihood of accidents. The unstandardized management of safety training will result in the personnel's lack of safety awareness, insufficient knowledge of special situation handling, and other knowledge, as well as the inability to provide timely and accurate guidance and assistance in the face of special aircraft situations, leading to unsafe incidents. Unreasonable personnel scheduling can lead to personnel fatigue or biological clock disorders at work, resulting in mistakes when commanding aircraft altitude and triggering altitude deviation. With fatigue scheduling, the stability and accuracy of approach operations will be affected, increasing the probability of unstable approaches. In the ISSA-ELM prediction model used in this paper, we have selected three typical unsafe events in the terminal area control system as the primary indicators, which usually have a key impact on the final terminal area operation safety situation, and that can directly reflect the core characteristics of the terminal area operation unsafe events, while the secondary indicators can provide complementary information, reflecting what kind of factors caused the terminal area operation unsafe events. By using the ISSA-ELM model to predict the probability of the occurrence of the first-level indicator time in the training process, the input data are mapped through the neurons in the hidden layer, and then the prediction results are obtained through the output layer. For an ELM with n input samples \(x_{i}(i=1,2,\cdots n)\) and L hidden layer neurons, its output can be expressed as \(y_{i}=\sum_{i=1}^{L}\beta_{i}g(\omega_{i}\cdot x_{j}+b_{i})\), where \(\omega_{i}\) is the connection weight between the input layer and the ith hidden layer neuron, \(b_{i}\) is the bias of the ith hidden layer neuron, and \(\beta_{i}\) is the connection between the i-th hidden layer neuron and the output layer. The connection weight between the i-th hidden layer neuron and the output layer, and \(g(\omega_{i}\cdot x_{j}+b_{i})\), is the activation function. It can be seen that \(\beta_{i}\) actually plays a weighting role. Different values of \(\beta_{i}\) will cause the hidden layer neurons to have different degrees of influence on the output results. In order to better identify which primary indicator has a large impact on system safety, we normalized the data of the secondary indicators and directly used them as input vectors of the model to obtain the risk probability of the primary indicators. Then, we introduced [PERSON] theorem to obtain the final value of the security situation.