气候变化领域集成服务门户(CCPortal): A fuzzy neural network bagging ensemble forecasting model for 72-h forecast of low-temperature chilling injury

CCPortal

DOI	10.1007/s11069-020-04393-y
	A fuzzy neural network bagging ensemble forecasting model for 72-h forecast of low-temperature chilling injury
	Lu H.; Ou Y.; Qin C.; Jin L.
发表日期	2021
ISSN	0921030X
起始页码	2147
结束页码	2160
卷号	105 期号:2
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.
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
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206388
作者单位	Climate Center of Guangxi Zhuang Autonomous Region, No.81 Minzu Ave, Nanning, Guangxi 530022, China
推荐引用方式 GB/T 7714	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],2021,105(2).
APA	Lu H.,Ou Y.,Qin C.,&Jin L..(2021).A fuzzy neural network bagging ensemble forecasting model for 72-h forecast of low-temperature chilling injury.Natural Hazards,105(2).
MLA	Lu H.,et al."A fuzzy neural network bagging ensemble forecasting model for 72-h forecast of low-temperature chilling injury".Natural Hazards 105.2(2021).