Tabular-to-Image Deep Learning for Multi-Factor Aviation Bird Strike Risk Prediction

Wildlife strikes are a persistent aviation safety concern because bird strike risk is shaped by dynamic interactions among weather, bird movement, airport operations, seasonality, and time of day. Although traditional machine learning methods have been used to model structured aviation safety data, they may not fully capture the temporal and cross-feature relationships that contribute to elevated risk. This study proposed a time-aware tabular-to-image deep learning framework for multi-factor aviation bird strike risk prediction. Hourly weather observations from Houston Intercontinental Airport (IAH) were integrated with NEXRAD radar-based bird risk data to construct a three-class prediction task: Low, Moderate, and Severe risk. The final dataset included 43,732 hourly observations. These classifications represent radar-derived avian hazard levels rather than confirmed bird strike events or damage severity. To preserve temporal structure, tabular weather, time, and train-derived risk prior features were transformed into 20 × 48 feature-by-time images representing a 48 h historical window. These images were used as inputs to a residual squeeze-and-excitation convolutional neural network (CNN) and compared against traditional statistical, machine learning, and baseline CNN models. The proposed 48 h weather history Res-CNN achieved 79.74% accuracy and a macro F1-score of 78.74%, compared with 80.02% accuracy and 79.09% macro F1 for LightGBM. Although the proposed model did not outperform the strongest tree-based baseline, it achieved competitive performance and substantially outperformed the original 4 × 5 feature grid CNN. These findings demonstrate the feasibility of incorporating structured temporal information into tabular-to-image deep learning while highlighting the continued effectiveness of tree-based methods for structured aviation risk data.