###### Abstract  
This study analyzes the impact of weather factors on the resilience of airport flight operations, focusing on flight performance, economic outcomes, and transportation capacity. A Kolmogorov-Arnold Network (KAN) model was employed to identify key weather variables and establish the relationship between these factors and airport operational resilience. Xi'an Xianyang International Airport was used as a case study, with the weights of various routes determined using grey relational analysis, considering average daily flight volume, flight distance, and airport flow stability indicators. Flight operation records and weather data were utilized to assess the influence of critical weather factors on key operational resilience metrics. The findings reveal that routes in economically developed areas exert a more pronounced effect on flow stability. Temperature and wind speed emerged as the most influential factors, with importance values of 0.35 and 0.32, respectively, about flight operations and economic performance. Furthermore, changes in wind direction and wind speed had the greatest impact on transportation capacity, with importance values of 0.7 and 0.65, respectively. These results highlight the need for special attention to weather factors such as temperature and wind speed during flight scheduling and risk assessment to ensure operational safety, efficiency, and economic viability. a +
Footnote †: journal: Applied sciences  
**C.**: [PERSON]; [PERSON]; [PERSON] The Importance of Weather Factors in the Resilience of Airport Flight Operations Based on Kolmogorov-Arnold Networks (KANs). weather-related disruptions in key airports can affect overall network resilience and the steps that can be taken to mitigate these impacts [5; 6]. The National Academy of Sciences defined resilience as the ability of a system to plan for, absorb, recover from, and adapt to adverse events [7]. Within the context of air traffic, resilience refers to the capacity of the system to prevent or minimize disruptions caused by various factors, including weather, and to recover from such events effectively. [PERSON] et al. extended this concept by introducing four dimensions for evaluating the resilience of critical infrastructure: detection, resistance, recovery, and adaptability [8]. These dimensions are particularly relevant for airports, which must remain operational during adverse conditions and recover quickly from disruptions to minimize the impact on passengers and airlines [9]. To assess the resilience of airport operations, researchers have developed Key Performance Indicators (KPIs) that measure the system's capacity, efficiency, and environmental performance. These KPIs, set by the International Civil Aviation Organization (ICAO), include metrics such as airport capacity (e.g., demand, traffic complexity, and air traffic control workloads), operational efficiency (e.g., flight delays and associated costs), and environmental factors (e.g., fuel consumption and greenhouse gas emissions due to delays) [10; 11]. These indicators provide a framework for quantifying the impact of disruptions on airport operations, thus helping stakeholders evaluate the effectiveness of mitigation strategies. Despite the development of sophisticated models to predict flight delays, including Artificial Neural Networks (ANNs), there are still significant gaps in predicting the impact of weather on aviation resilience. Most studies have focused on extreme weather events, such as hurricanes, thunderstorms, and snowstorms, which are well-documented and often predictable [12; 13]. However, gradual weather changes, which occur more frequently, can significantly impact airport operations by reducing runway capacity and increasing delays. For example, changes in wind direction and speed, temperature fluctuations, and variations in visibility due to fog or cloud cover can disrupt the scheduling and operation of flights [14].