DOI: 10.1016/j.atmosenv.2019.117200
论文题名: An enhanced interval PM2.5 concentration forecasting model based on BEMD and MLPI with influencing factors
作者: Wang Z. ; Chen L. ; Ding Z. ; Chen H.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 223 语种: 英语
英文关键词: Multilayers
; Pollution
; Signal processing
; Bivariate empirical mode decompositions
; Environmental Monitoring
; Grey incidence analysis
; Interval forecasting
; Intrinsic Mode functions
; PM2.5 concentration
; Pollutant concentration
; Reconstruction techniques
; Forecasting
; accuracy assessment
; atmospheric modeling
; atmospheric pollution
; concentration (composition)
; decomposition analysis
; early warning system
; empirical analysis
; environmental factor
; environmental monitoring
; forecasting method
; particulate matter
; public health
; reconstruction
; article
; China
; empirical mode decomposition
; environmental monitoring
; forecasting
; incidence
; intrinsic mode function
; multilayer perceptron
; time series analysis
; Beijing [China]
; China
学科: Bivariate empirical mode decomposition
; Interval forecasting
; Interval multilayer perceptron
; Mode reconstruction
; PM2.5 concentration
中文摘要: In order to protect public health by providing an early warning of harmful air pollutants, various forecasting models are proposed to forecast the average values of daily pollutant concentrations. In fact, even on the same day, the concentration of pollutants will fluctuate greatly during different time periods, point-based models can not reflect the variability well. Thus, an enhanced interval PM2.5 concentration forecasting model is developed in this paper, which is based on interval decomposition ensemble and considering influencing factors. For the purpose of obtaining main influencing factors, interval grey incidence analysis (IGIA) is used to select input variables for model. The interval-valued time series (ITS) of PM2.5 concentration and its influencing factors are decomposed into a finite number of complex-valued intrinsic mode functions (IMFs) and one complex-valued residual by bivariate empirical mode decomposition (BEMD) algorithm. Considering the different amounts of various IMFs, the complex-valued IMFs and residual are clustered into fewer classes by reconstruction technique. Then, interval multilayer perceptron (MLPI) is employed to fit the lower and upper bound simultaneously of all classes to obtain the corresponding forecasting results, which are combined to generate the aggregated interval-valued output by a simple addition approach. The model is tested by the dataset collected from three environmental monitoring stations in Beijing, China. Experimental results show that the enhanced model outperforms other considered models by means of forecasting accuracy and stability. © 2019 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160586
Appears in Collections: 气候变化与战略
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作者单位: School of Mathematical Sciences, Anhui University, Hefei, Anhui 230601, China; School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
Recommended Citation:
Wang Z.,Chen L.,Ding Z.,et al. An enhanced interval PM2.5 concentration forecasting model based on BEMD and MLPI with influencing factors[J]. Atmospheric Environment,2020-01-01,223