Time-Series Prediction of Intense Wind Shear Using Machine Learning Algorithms: A Case Study of Hong Kong International Airport

Machine learning algorithms are applied to predict intense wind shear from the Doppler LiDAR data located at the Hong Kong International Airport. Forecasting intense wind shear in the vicinity of airport runways is vital in order to make intelligent management and timely flight operation decisions. To predict the time series of intense wind shear, Bayesian optimized machine learning models such as adaptive boosting, light gradient boosting machine, categorical boosting, extreme gradient boosting, random forest, and natural gradient boosting are developed in this study. The time-series prediction describes a model that predicts future values based on past values. Based on the testing set, the Bayesian optimized-Extreme Gradient Boosting (XGBoost) model outperformed the other models in terms of mean absolute error (1.764), mean squared error (5.611), root mean squared error (2.368), and R-Square (0.859). Afterwards, the XGBoost model is interpreted using the SHapley Additive exPlanations (SHAP) method. The XGBoost-based importance and SHAP method reveal that the month of the year and the encounter location of the most intense wind shear were the most influential features. August is more likely to have a high number of intense wind-shear events. The majority of the intense wind-shear events occurred on the runway and within one nautical mile of the departure end of the runway.

Automated summary

Machine learning algorithms were used to predict intense wind shear at Hong Kong International Airport using Doppler LiDAR data. Accurate forecasting of intense wind shear near airport runways is crucial for intelligent management and timely flight operations. Bayesian-optimized machine learning models, including adaptive boosting, light gradient boosting machine, categorical boosting, extreme gradient boosting, random forest, and natural gradient boosting, were developed to predict the time series of intense wind shear. Among the models tested, the Bayesian optimized-Extreme Gradient Boosting (XGBoost) model outperformed others with the lowest mean absolute error (1.764), mean squared error (5.611), root mean squared error (2.368), and highest R-Square (0.859). The XGBoost model was further interpreted using the SHapley Additive exPlanations (SHAP) method, which revealed that the month of the year and the location of the most intense wind shear were the most influential features. August exhibited a higher likelihood of experiencing intense wind shear, and most of these events occurred on or near the runway's departure end within one nautical mile.