# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 4 Case Study Analysis  
For the selection of the population size, we started with a smaller population size, trying population sizes of 10, 20, 30, etc., recorded the convergence of the ISSA in the optimized limit learning machine and the accuracy of the final evaluation results in each experiment, and selected a population size that balanced performance and computational cost based on the experimental results (30). The number of iterations is not limited due to the fact that the security assessment of the end-area control system usually needs to be completed within a certain period of time, so it cannot be increased without limit. Therefore, a suitable number of iterations (100) was chosen to minimize the computational time while ensuring convergence. This research gathered 200 sets of sample data from a terminal location in North China. We employed 19 secondary indicators within the liveware, the hardware, the environment, and the management paradigm as input vectors. We used the principal indicators--the likelihoods of height deviation, closeness to air hazards, and unstable approach incidents--as output vectors for the ISSA-ELM model. Front-line safety management staff noted the input indications, while air traffic control automation systems supplied the output indicators. The total number of training samples was 120, which was determined after a number of considerations and trade-offs. On the one hand, according to the complexity of the research problem and the diversity of the data, too few samples may result in the model not being able to fully learn the potential patterns in the data, thus resulting in the phenomenon of underfitting, while too many samples may help the model to learn richer information, but may increase the consumption of computational resources and the training time. The training sample data mainly comes from the integration of multiple data sources, such as the operation records of the terminal area control system, security event reports, equipment \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Model** & **RMSE** & **MAPE** & **MAE** & **R2** \\ \hline ISSA-ELM & 2.6008 & 8.6119 & 2.0041 & 0.9060 \\ SSA-ELM & 3.1499 & 8.6370 & 2.0090 & 0.7382 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the ISSA-ELM and SSA-ELM prediction accuracy. monitoring data, and related meteorological information. For the in-depth analysis of the operation mechanism and safety-influencing factors of the terminal area control system, we carefully selected 19 first-level indicator representative features from the multi-source data as the input variables of the training samples. These indicators cover the four dimensions of people-equipment-environment-management. For the output indicators, three typical unsafe events of terminal area control systems were selected. Before constructing the training samples, rigorous preprocessing operations were performed on the raw data to ensure the quality and usability of the data. First, for the missing value processing in the data, a filling method based on statistical analysis and data mining techniques was used. For the missing values of numerical features, the mean, median, or predicted values based on regression models were used to fill in according to the distribution pattern of the features in other complete data records; for the missing values of subtyped features, they were filled in according to their plurality in the dataset or predicted categories based on classification models. Second, in order to eliminate the effect of differences in magnitude and the order of magnitude between different features on the model training, all of the input features were normalized. The normalization process makes all of the features have the same starting point weights in the model training process, which improves the training efficiency and accuracy of the model. We partitioned the sample data into the following two segments: the training set, which constituted 80% of the data, and the testing set, which comprised 20%.