Spatiotemporal Analysis and Prediction of Avian Migration Under Climate Change

Frequent bird strikes during peak migration periods pose a significant risk to aviation safety. Existing prevention methods rely on static historical patterns and lack the ability to adapt to real-time changes. Short-term meteorological fluctuations are crucial in shaping bird migration behavior, influencing both its timing and intensity. Climate change increases the variability of these factors, making predictions more difficult. Simple models may describe migration patterns under stable conditions but struggle to capture the complexity introduced by climate-driven fluctuations. To address this, we propose a model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and an attention mechanism, achieving prediction accuracy consistently above 0.9. CNN extracts features, LSTM captures temporal dependencies, and attention assigns weights to important features. Unlike traditional statistical methods, this model transitions from traditional heuristic approaches to data-driven quantitative forecasting, offering insights into migration intensity while accounting for meteorological fluctuations influenced by climate change. Ablation experiments showed that removing the attention mechanism, CNN module, and both components reduced the average prediction accuracy by 3.93%, 8.47%, and 10.96%, respectively. These results demonstrate that bird migration predominantly occurs at night and is significantly influenced by radiation levels and wind conditions. This research incorporates meteorological variability into predictive modeling to develop data-driven strategies for enhancing aviation safety. Additionally, it addresses environmental challenges and promotes sustainable practices by optimizing flight schedules to reduce bird strikes, improve fuel efficiency, and minimize emissions. This approach also contributes to ecological conservation and supports sustainability goals.