Near-surface maximum wind speed prediction of XGBoost model based on k-nearest neighbor mutual information feature selection

Abstract This study focused on predicting the near-surface maximum wind speed using the eXtreme Gradient Boosting (XGBoost) model based on k-nearest neighbor mutual information feature selection. The data from 93 meteorological stations in Guangxi Province from 2016 to 2021, with a temporal resolution of 3 h, were used for the prediction. By examining the effects of various dynamic and thermal factors, such as high altitudes and surface variables, on the prediction of maximum wind speed, a novel XGBoost-based prediction model for maximum wind speed was proposed. The model incorporates the k-nearest neighbor mutual information feature selection algorithm to choose the most relevant factors for accurate wind speed prediction. In the design of the prediction model, there are two main areas of improvement. First, a stepwise variable selection algorithm based on k-nearest neighbor mutual information estimation was employed, which selects relevant variables and removes weakly relevant variables through two steps, effectively eliminating redundant prediction characteristics that affect accuracy by screening the primary predictors and retaining important forecasting factors. Second, the Bayesian optimization algorithm was used to optimize the parameters in the XGBoost model, significantly enhancing the model's generalizability. The optimized and improved prediction model was utilized to model and research the near-surface maximum wind speed for 6 forecast lead times (12–72 h) at 93 meteorological stations. Comparative results of various forecast experiments using independent prediction samples from 2020 to 2021 demonstrated that the new model reduced the average mean absolute error (MAE) evaluation metric by 18.9–30.06% for the prediction results of the 93 stations. The root mean square error (RMSE) metric decreased by 40.18–65.83%. For the prediction of maximum wind speeds exceeding level 6, the MAE was reduced by 40.41%, 25.93%, 19.96%, 21.39%, 12.39%, and 8.55% for the 6 forecast lead times, respectively. The RMSE evaluation metric also decreased by 30.92%, 18.67%, 12.29%, 12.21%, 7.92%, and 2.39% for the respective lead times. The improved model demonstrated consistent prediction performance and significantly enhanced accuracy.