## 5 Conclusions  
This study investigates the effects of weather factors on the resilience of airport flight operations, focusing on operational performance, economic efficiency, and transportation capacity. Using the Kolmogorov-Arnold Network (KAN) model, we successfully predicted flight resilience based on weather data and flight operation records from Xi'an Xianyang International Airport. The model's excellent predictive performance, reflected in metrics such as its high R\({}^{2}\) value of 99.806%, demonstrates its ability to capture nonlinear relationships between weather conditions and operational resilience effectively. The results show significant differences in resilience across different routes and regions. Routes in economically developed areas, such as those in eastern China, exhibit higher resilience and lower fluctuations, maintaining operational stability even under high flight volumes. Conversely, routes in western and remote areas experience more significant fluctuations in resilience values, reflecting lower operational stability and greater vulnerability to disruptions. Regarding operational resilience, routes in the eastern region of China show more consistent and stable resilience metrics, suggesting that better infrastructure and more sophisticated management systems help mitigate the impact of adverse weather conditions. Figure 16: The importance of weather factors to flight transportation capacity. In contrast, routes in the western and remote regions are less resilient, highlighting the need for improved airport facilities and flight scheduling mechanisms. Key weather factors were identified as significant flight determinants of resilience. Temperature and wind speed emerged as the most influential factors in operational and economic performance, with importance values of 0.35 and 0.32, respectively. On the other hand, wind direction change and wind speed were the primary contributors to variations in transportation capacity, with importance values of 0.7 and 0.65, respectively. The transferability of this approach includes the resilience calculation method and the KAN model. The resilience calculation method used in this study can be applied to different metrics of an airport or transportation hub affected by other factors. The key to making this approach transferable is customizing the input data to reflect local conditions and operational characteristics. For example, the time window of resilience calculation should be divided to align with the recording intervals of weather data. The KAN model offers potential transferability to other airports and case studies, providing appropriate local data. Applying the KAN model can help analyze the resilience of systems similarly impacted by other factors for surface transportation nodes, allowing for scheduling optimization and minimizing disruptions. However, adjusting the model to account for the different operational characteristics requires adjusting. For example, trains are less affected by wind direction and wind speed than airplanes. Therefore, while rail stations experience delays related to weather conditions, the nature and frequency of disruptions might differ, necessitating additional model adaptations. These findings provide valuable insights for airport and airline managers, who can leverage these data to enhance flight scheduling, minimize delays, and improve airport resilience to weather disruptions. Advanced models like the KAN enhance operational efficiency and the overall resilience of the aviation network. However, this study has certain limitations. Notably, the flight delay data used in this study does not separate delays into specific phases (taxing, take-off, and landing). As a result, the individual impact of weather factors on these phases was not calculated, limiting the precision of optimization strategies. Additionally, while the KAN model performs well with the current dataset, its effectiveness in different regions or under extreme weather conditions needs further validation.