DOI: 10.1007/s11069-020-04393-y
论文题名: A fuzzy neural network bagging ensemble forecasting model for 72-h forecast of low-temperature chilling injury
作者: Lu H. ; Ou Y. ; Qin C. ; Jin L.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 105, 期: 2 起始页码: 2147
结束页码: 2160
语种: 英语
中文关键词: Bagging ensemble forecast
; Cold–wet index
; Fuzzy neural network
; Short-term forecast
英文关键词: artificial neural network
; ensemble forecasting
; fuzzy mathematics
; injury
; low temperature
; numerical model
; precipitation (climatology)
; China
; Guangxi Zhuangzu
英文摘要: 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-h 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. 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. © 2020, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169187
Appears in Collections: 气候变化与战略
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作者单位: Climate Center of Guangxi Zhuang Autonomous Region, No.81 Minzu Ave, Nanning, Guangxi 530022, China
Recommended Citation:
Lu H.,Ou Y.,Qin C.,et al. A fuzzy neural network bagging ensemble forecasting model for 72-h forecast of low-temperature chilling injury[J]. Natural Hazards,2021-01-01,105(2)