Research on machine learning-based modeling method for forecasting Maximum wind speed of typhoon in Guangxi

Aiming at the lack of nonlinear intelligent computational modelling methods for the fixed-point and quantitative forecasting of typhoon gales in the current numerical forecast products, the paper takes the daily extreme winds at five typical representative meteorological stations (Guilin, Wuzhou, Longzhou, Nanning, Yulin) as the forecast object, and carries out the construction of a daily extreme wind forecast model based on multivariate linear regression (MR), support vector machine (SVM), fuzzy neural network (FNN), and the ground-based observation and reanalysis of the data of the typhoon in the past 40 years during the typhoon impact in Guangxi. The construction of the daily maximum wind prediction model based on multiple linear regression (MR), support vector machine (SVM) and fuzzy neural network (FNN) is carried out. The test results of the independent samples show that the FNN model has the smallest mean absolute error for the four stations of Guilin, Wuzhou, Longzhou, and Yulin in terms of the mean absolute error of the full-sample wind speed forecast, and the best overall forecast accuracy, while the MR forecast model has a better forecast capability for Nanning station, and the SVM model has an overall bias in the forecast effect, in which the FNN forecast model has a 1% to 1% reduction in mean absolute error compared with that of MR. The mean absolute errors of the FNN forecast model are reduced by 1%-29% (except for Nanning station); the mean absolute errors of the FNN forecast model are reduced by 6%-29% compared with the SVM forecast model. the mean absolute errors of the MR forecast model are reduced by 5%-13% compared with the SVM forecast model (except for Guilin station). The statistical results of the four evaluation indexes, including TS score, hit rate, null rate and forecast bias, for winds of magnitude 6 or above show that the FNN model has the highest and relatively stable prediction accuracy, followed by the MR scheme, and the SVM has the worst prediction effect among the three schemes. The fuzzy neural network has certain applicability to the prediction of very high wind speed, which can be a good reference for the prediction of daily very high wind speed on the ground during typhoons in Guangxi, and can provide theoretical references and empirical evidence basis for the later development of the research on the prediction of high wind disasters in Guangxi.