Estimating Turbulence Due to Low-Level Wind Shear in Airport Runway Zones Using TabNet-SHAP Framework

In this research, we present an advanced predictive framework designed to assess the turbulence induced by low-level wind shear near the runways at Hong Kong International Airport (HKIA), utilizing data from Pilot Reports (PIREP). This framework integrates the TabNet architecture with SHapley Additive exPlanations (SHAP), thereby enhancing both predictive accuracy and interpretability. Given the imbalance in the PIREP data, we implement various data augmentation techniques and employ Bayesian optimization to fine-tune the hyperparameters of the TabNet model. Our analysis revealed that the TabNet model, when applied to data balanced with the Support Vector Machine - Synthetic Minority Over-sampling Technique (SVM-SMOTE), demonstrated superior performance, evidenced by a Geometric Mean (G-Mean) of 0.74, a Matthews Correlation Coefficient (MCC) of 0.37, a Balanced Accuracy (BA) of 0.74, and an Area Under the Receiver Operating Characteristic (AU-ROC) of 0.739. SHAP analysis further enhanced the interpretability of the TabNet model by identifying key contributing factors, including the magnitude of wind shear and altitude, which significantly influence the likelihood of significant turbulence occurrence. Specifically, SHAP insights demonstrated the critical impact of wind shears between 15 and 25 knots and the pronounced effects of complex terrain and sea breezes on significant turbulence occurrence, predominantly at altitudes below 1200 feet. These findings not only demonstrate the efficacy of the TabNet-SHAP model in enhancing aviation safety through improved turbulence prediction but also provide actionable insights for operational and safety protocols at airports prone to low-level wind shear.