Explainable Learning Framework for the Assessment and Prediction of Wind Shear-Induced Aviation Turbulence

Wind shear-induced aviation turbulence (WSAT) remains a major safety concern during approach and takeoff phases at complex terrain airports. This study develops an interpretable Explainable Boosting Machine (EBM) framework to classify WSAT events at Hong Kong International Airport (HKIA). The framework integrates Differential Evolution with HyperBand (DEHB) for hyperparameter tuning and applies multiple data balance methods such as SMOTE, Borderline SMOTE, Safe-Level SMOTE, and G-SMOTE. The dataset consists of Pilot Reports (PIREPs) collected between 1 January 2007 and 31 July 2023, with 6838 wind shear events that include variables that relate to wind shear magnitude, altitude, runway distance, rainfall condition, and causal factors. Among all configurations, the EBM tuned via DEHB and trained with SMOTE-treated data achieved the highest predictive performance with BA = 0.710, MCC = 0.321, and G-Mean = 0.708, higher than untreated and other balance variants. EBM-based interpretation showed that wind shear altitude and wind shear magnitude were key predictors, and their interaction reflected a nonlinear pattern where WSAT probability rose under moderate-to-high shear conditions (wind shear altitude ≈ 0.5–2.5 and magnitude ≈ 30–35 knots). The DEHB-optimized EBM–SMOTE framework provides a transparent interpretive foundation for WSAT risk assessment and advances quantitative evaluation in aviation meteorology.