IATCN: Interpretable Attention-Enhanced Temporal Convolutional Network for Multiple Flight Safety Incidents Prediction

Flight safety incidents, such as hard landings and tail strike risks, represent critical concerns during the landing phase. Although Quick Access Recorder (QAR) systems collect extensive multivariate flight data, previous studies have faced challenges in effectively modeling temporal dependencies and providing model interpretability, limiting their ability to predict multiple safety incidents simultaneously. To address this issue, we propose a novel model, named IATCN (Interpretable Attention-Enhanced Temporal Convolutional Network), for predicting multiple flight safety incidents, including both hard landing and tail strike risk incidents. Specifically, IATCN combines temporal convolutional networks (TCNs) with attention mechanisms, enabling the model to effectively capture temporal dependencies in flight data while enhancing the focus on key features. Additionally, we are the first to integrate attention mechanisms with Grad-CAM to improve the interpretability and visualization of flight safety incidents. This combination offers valuable insights into the model’s decision-making process by highlighting the importance of specific flight parameters. We conduct experiments on real-world QAR datasets of 37,904 Airbus A320 flight samples. Experimental results demonstrate that the IATCN model outperforms state-of-the-art baselines and provides effective interpretability by visualizing the importance of parameters, offering valuable insights for flight safety analysis.

Recommended citation: Song, L., Sun, H., Li, X., et al. (2025). IATCN: Interpretable Attention-Enhanced Temporal Convolutional Network for Multiple Flight Safety Incidents Prediction.(Submitted)