## 3 Methodology
### Multi-Channel Traffic and Weather Scene Image
#### 3.1.2 Construction of Images  
(1) Rasterization of terminal areas  
Convective weather in the terminal area is a major factor contributing to airport delays, especially when a high volume of flights is scheduled. In addition, the relative position \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study Area** & **Research Focus** & **Key Features** & **Unaddressed Gaps** \\ \hline Weather Delay Index (WITI) & WITI, E-WITI, FCM ([PERSON], [PERSON], [PERSON] [1, 2, 3, 4, 5], [PERSON] et al. [8]) & Statistical indicators; regression models; sector-level delay estimates under convective weather & \textless{1} Lacks spatially explicit weather trajectory modeling; \textless{2} Focus on national/airport-scale trends, limited flight-level granularity \\ Deep Learning for Weather Forecasting & CNN, ConvLSTM, Broad UNet, CNN-BiLSTM-Attention ([PERSON] et al. [11], [PERSON] et al. [12], [PERSON] et al. [15]) & Multichannel radar/satellite input; spatiotemporal dynamics; attention mechanisms & \textless{1} Focus on pure weather prediction, not directly linked to flight trajectory or delay; \textless{2} Lack of operational integration in multi-airport scenarios & \textless{1} Most models use structured tabular data (visibility, wind, etc.), not unstructured image data; \textless{2} Spatial correlations between convective patterns and trajectory paths are weakly modeled \\ Integrated Traffic-Weather Learning Models & Hybrid CNN-GRU; GraphCast; MethSet-2 [18, 19, 20] & Ultra-short-term, high-resolution forecasts with multiscale features & \textless{1} Limited exploration of real-scene imagery (e.g., radar/traffic images) in delay prediction tasks; \textless{2} Models seldom generalize across airport networks under dynamic weather scenes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of research on airport delay prediction. between the convective weather and flight paths plays a key role in determining the severity of the impact. For this reason, this paper proposes a new data representation method called multi-channel air traffic and weather scene images to portray the impact of convective weather within the terminal area on flight operations and to serve as an input to airport delay prediction models. Each image is represented as a two-dimensional matrix. Therefore, the target terminal area is first rasterized to serve as the foundation for subsequent image construction [26]. To ensure consistency and ease of processing, the outer square boundary of the terminal area is selected as the image extent and divided into 125 \(\times\) 125 grids (each 2 km wide) (see Figure 2). Since each grid cell contains latitude and longitude information, the aircraft's location within the airspace can be assigned to the corresponding grid, which allows us to populate the grid with traffic operation information, such as traffic volume, average flight altitude, average flight speed, etc. However, because each grid cell can store only one value, multiple 2D matrices are employed to represent diverse traffic and weather information. These various 2D matrices together form a multi-channel image that reflects various aspects of traffic and weather conditions in the same area. By combining these channels, a realistic picture of the air traffic situation is created. Figure 1: Procedural Framework for Constructing MTWSI. (2) Visualization of traffic flow features  
Since traffic scenarios span a period rather than a single moment, and delay levels are not instantaneous, we aggregate all traffic and convective weather data over the designated period onto a grid map. Specifically, by assigning the flight track data to the corresponding grids of the 2D matrix, the visualization will display the number of flights passing through each grid during a certain period of time, as well as the overall flight status of the flights. Three types of images are based on flight trajectory, altitude, and speed information.