A Hybrid VMD-TPE-TCN Framework for Wind Shear Prediction near an Airport Runway

Wind shear presents a critical challenge to aviation safety, particularly during aircraft takeoff and landing, necessitating precise and timely forecasting to mitigate operational risks. This paper introduces a hybrid Variational Mode Decomposition (VMD) framework integrated with a Tree-structured Parzen Estimator (TPE)-optimized Temporal Convolutional Network (TCN) for accurate wind shear magnitude prediction. The model utilizes Doppler LiDAR data collected from January 2020 to December 2021 from two Doppler LiDAR systems installed at Hong Kong International Airport (HKIA). The VMD technique decomposes wind shear signals into intrinsic mode functions (IMFs), which are individually modeled using the TCN, effectively capturing both short-term variations and long-term dependencies. TPE-based hyperparameter tuning optimizes key parameters, further enhancing the predictive accuracy of the proposed framework. A comparative evaluation against VMD-TPE-LSTM, VMD-TPE-BiLSTM, VMD-TPE-GRU, and VMD-TPE-BiGRU demonstrates that the VMD-TPE-TCN(IMF 1-2-4-5) framework achieves the best performance. It shows a lower Mean Absolute Error (MAE) of 6.50, Root Mean Square Error (RMSE) of 2.04, and Theil’s U Statistic of 0.1270, reflecting better predictive accuracy. These outcomes affirm the capability of the VMD-TPE-TCN framework to provide reliable wind shear forecasts, enhancing aviation safety, operational planning, and risk management within airport environments.