## 5 Conclusions  
This study introduces a multi-channel, image-based method that automatically captures complex, weather-affected traffic features to predict airport delay. The proposed method comprises three main components:  
(1) Fuzzy C-Means (FCM) clustering is applied to classify airport departure delay levels. (2) Weather Advective Flow (WAF) data, combined with incoming and outgoing flight data, are rasterized to construct a multi-channel image that portrays air traffic operation scenarios under convective weather conditions. (3) Deep learning techniques analyze features in the generated images to predict airport delay levels. Experimental results demonstrate that the proposed method achieves significantly higher prediction accuracy compared to traditional machine learning methods. The proposed model significantly enhances the accuracy of delay prediction and offers effective support for airport operational decisions. It facilitates proactive resource allocation, schedule adjustments, and reduced passenger waiting times. Furthermore, the model proves highly applicable in real-world scenarios, particularly in capturing the impact of complex weather on air traffic. Its ability to improve operational efficiency \begin{table}
\begin{tabular}{c c c c} \hline \hline  & 5 \(\times\) 5 & 25 \(\times\) 25 & 125 \(\times\) 125 \\ \hline Training time & 2 ms/step & 9 ms/step & 238 ms/step \\ Testing time & 1 ms/step & 3 ms/step & 58 ms/step \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Model training duration at different resolutions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Resolution**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Label**} & \multicolumn{2}{c}{**Classification Report**} & \multicolumn{2}{c}{**Confusion Matrix**} \\ \cline{3-6}  & & & **Precision** & **Recall** & **F1** & **A0** & **A1** \\ \hline \multirow{3}{*}{\(5\times 5\)} & \multirow{3}{*}{89.25\%} & A0 & 0.91 & 0.98 & 0.94 & 1323 & 30 \\  & & A1 & 0.56 & 0.22 & 0.32 & 134 & 38 \\ \multirow{3}{*}{\(25\times 25\)} & \multirow{3}{*}{91.61\%} & A0 & 0.93 & 0.98 & 0.95 & 1324 & 29 \\  & & A1 & 0.72 & 0.42 & 0.53 & 99 & 73 \\ \multirow{3}{*}{\(125\times 125\)} & \multirow{3}{*}{91.21\%} & A0 & 0.92 & 0.99 & 0.95 & 1333 & 20 \\  & & A1 & 0.74 & 0.34 & 0.47 & 114 & 58 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction results of cumulative delay on multichannel images of different resolutions. and reduce management costs underscores its strong practical value and potential for broader adoption. Next steps could explore these key areas:  
(1) Designing more complex and effective networks to further enhance prediction performance. (2) Integrating clustering methods with domain knowledge and professional experience to improve explanatory power in airport delay classification. (3) Incorporating advanced temporal feature extraction techniques to better capture the time-dependent characteristics inherent in air traffic scenarios. (4) Considering the method's integration with existing operational protocols such as those of ICAO or local air traffic management systems, the current approach remains at the research stage and has not yet been embedded in formal procedures. Conceptualization, L.Y. ; methodology, L.Y., L.K. and H.C.; software, H.C.; formal analysis, L.Y. and L.K. ; investigation, L.Y. and H.C.; resources, L.Y. and H.C.; data curation, L.Y. ; writing--original draft preparation, L.K. and L.Y. ; writing--review and editing, L.Y., L.K. and H.C.; supervision, L.Y. and H.C.; project administration, L.Y. ; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript. This paper is supported by the National Key R&D Program of China (No. 2022 YFB2602400). The raw data supporting the conclusions of this article will be made available by the authors on request. We would like to thank the State Key Laboratory of Air Traffic Management System for providing the data used in the model tests described in this paper. The authors declare no conflicts of interest.