###### Abstract
These weather changes are often more challenging to predict and manage because they do not trigger immediate emergency responses but can gradually erode the efficiency of airport operations [15]. In China, the impact of weather on airport operations is particularly relevant, given the country's rapid growth in air traffic and the increasing importance of its major airports. Xi'an Xianyang International Airport, one of the largest airports in Northwest China, plays a crucial role in connecting the region to both domestic and international destinations. However, due to its geographical location, the airport is exposed to various weather conditions, including extreme temperatures, dust storms, and fog [16; 17]. These weather events pose significant challenges to airport operations, leading to frequent delays and cancellations. Despite the importance of Xi'an Xianyang International Airport as a regional hub, there has been relatively little research on the resilience of its operations in the face of weather-related disruptions. This study aims to address this gap by applying an advanced predictive model, the Kolmogorov-Arnold Network (KAN), to assess the impact of weather on the resilience of airport flight operations. The KAN model is particularly suited for tasks involving time-series predictions and function approximation, making it ideal for analyzing how weather patterns affect flight schedules. Unlike traditional neural networks, which rely on fixed activation functions at nodes, the KAN model employs learnable, spline-based activation functions along the network's edges, causing these delays and allowing it to capture nonlinear relationships more effectively [18]. This unique architecture enhances the accuracy of predictions. It improves the interpretability of the results and causes of these delays, thus making it easier to identify the critical weather factors influencing flight operations [19]. Using the KAN model to analyze weather data and flight operation records from Xi'an Xianyang International Airport, this study seeks to provide a comprehensive understanding of how weather conditions impact the resilience of airport operations. Specifically, this study identifies the critical weather variables influencing flight delays, cancellations, and overall airport capacity. The Grey Relational Analysis (GRA) method is used to quantify the importance of weather factors, such as wind speed, temperature, and visibility, in determining the resilience of flight operations [20]. Previous studies have highlighted the importance of weather factors such as wind speed and direction, temperature, and visibility in influencing airport capacity and flight delays [21]. For example, [PERSON] found that extreme weather events primarily cause flight delays in major U.S. airports [22]. Similarly, [PERSON] et al. used a Bayesian Network approach to analyze the impact of extreme weather on flight delays at Madrid-Barajas Airport, cause of these delays, thus demonstrating that different stages of flight operations, such as final approach and taxiing, are affected to varying degrees by weather conditions [23]. However, these studies primarily focus on extreme events and the cause of these delays, leaving a gap in understanding how gradual weather changes affect airport resilience. This study addresses systemic resilience in aviation networks and focuses on gradual weather changes. As noted earlier, most existing research has concentrated on individual flights or specific airports, with limited attention on the broader network effects of weather disruptions [24; 25]. This is particularly important for scale-free networks such as ATNs, where disruptions in hub airports can have far-reaching consequences for the entire system [25]. By analyzing how weather-related delays at Xi'an Xianyang International Airport affect the overall network, this study provides valuable insights into the systemic resilience of China's aviation infrastructure. The findings of this study have important implications for policymakers and industry stakeholders.