ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 49 Issue 6 > 2023,49(6):721-732. DOI:10.7519/j.issn.1000-0526.2022.092601 Prev Next
Forecasting of Extreme Wind Speed in Yanqing Competition Zone of the Winter Olympic Games Based on Ensemble Learning Algorithm
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Abstract:
Alpine skiing is extremely sensitive to wind, and especially extreme wind speed is often one of the key factors that determine the smooth progress of the Winter Olympic Games. The numerical model data from the European Center for Medium-Range Weather Forecasts (ECMWF) and corresponding extreme wind observations at eight key stations in Yanqing Competition Zone of the Winter Olympic Games from January to March during 2018-2021 are used. The objective forecasting models of extreme wind speed are constructed based on three types of machine learning algorithms: decision tree (DT), random forest (RF) and gradient boosting decision tree (GBDT). The comparative evaluation results show that the best predictors of extreme wind speed mainly focus on the wind speed and direction at different levels, and additionally include the vertical velocity at individual stations. Removing the wind direction leads to the decrease of accuracy and increase of mean absolute error (MAE) in most of models. On the whole, the GBDT and RF models based on the decision tree ensemble learning are superior to the single decision tree model (DT). The GBDT model has the least MAE ranging from 1.56 m·s-1 to 3.57 m·s-1, and the maximum improvement rate is up to 8.7% compared with the DT model. Besides, the GBDT model is also skillful in the forecasts of super threshold extreme wind speed. All models have the increasing trend in the MAE and decreasing trend in the accuracy with the rising elevation of stations. As the forecasting lead time extends, the MAE of each model shows a periodic diurnal variation. Based on the stacking ensemble learning method, the RGL model is established using the two outstanding models, GBDT and RF, as the primary learner and the support vector machine as the secondary learner. The results indicate that compared with the single model, the RGL model has a certain ability to improve the prediction of extreme wind speed, especially at the high-altitude stations with relatively high winds. The MAE can be reduced by a maximum of 0.13 m·s-1, and the accuracy can be increased by a maximum of 0.022. The relevant research results have been well applied to the 2022 Beijing Winter Olympic and Paralympic Games.
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