Assessment of Wind Shear Severity in Airport Runway Vicinity using Interpretable TabNet approach and Doppler LiDAR Data

Abstract

ABSTRACT Severe wind shear events near airport runways pose serious safety risks and are a growing concern in civil aviation. Identification of severe wind shear risk factors may enhance aviation safety. Although rare, severe wind shear impacts safety by affecting the airspeed, lift, and maneuverability of aircraft. This study presents TabNet, a novel deep learning technique coupled with Bayesian optimization (BO) to predict wind shear severity in the runway vicinity using Doppler LiDAR data from Hong Kong International Airport. To address imbalanced wind shear data, it was first processed by resampling techniques and then used as input to TabNet. The analysis demonstrated that Bayesian-tuned TabNet (BO-TabNet) with SVM-SMOTE-processed data led to better performance compared to other strategies. The TabNet architecture employs the attention mechanism to enable model-specific interpretability. Analysis showed that the most important contributing factor was the summer season, followed by the wind shear encounter location (1 nautical miles from the runway at the departure end). Additionally, a more comprehensive model-agnostic LIME method was used to elucidate the model from a local perspective. By predicting severe wind shear and assessing contributing factors, aviation stakeholders can proactively manage and mitigate the associated risks, leading to safer and more efficient operations.