A Fuzzy Neural Network Bagging Ensemble Forecasting Model for 72-hour Forecast of Low-temperature Chilling Injury

On the basis of the daily temperature and precipitation data of Guangxi and the NCEP/NCAR reanalysis data and forecast field data, the paper aims to determine the significant nonlinearity and temporal variability of the forecast quantity series and the overfitting that can easily appear in the forecast modeling of a single fuzzy neural network model and many adjustable parameters that are difficult to determine objectively. Thus, an ensemble forecasting model of fuzzy neural network bagging for 72-hour forecast of low-temperature chilling injury is developed. The forecast results of independent samples show that under the same forecast modeling sample (N = 299) and forecasting factor (M = 9), the fuzzy neural network bagging ensemble forecasting model obtains a mean absolute error of 13.91. By contrast, the mean absolute errors of the single fuzzy neural network forecasting model and the linear regression forecast are 15.82 and 18.13, respectively. The fuzzy neural network bagging ensemble forecast error is lower by 12.07% and 23.27%, respectively, compared with the latter two methods, showing a better forecasting skill. This improved performance is mainly due to the ensemble individuals of the fuzzy neural network bagging ensemble forecasting model with playback sampling. Different ensemble individuals are obtained. Analyses of the new scheme suggests that the forecast accuracy of the ensemble prediction model has been improved by enhancing the prediction ability and population diversity of the individual ensemble members. Therefore, the generalization capacity of the intelligent computing ensemble prediction model has been significantly enhanced. The ensemble enhances the generalization performance and forecast stability of the fuzzy neural network bagging ensemble forecasting model. Thus, this model has better applicability in forecasting nonlinear low-temperature chilling injury.