气候变化领域集成服务门户(CCPortal): An enhanced interval PM2.5 concentration forecasting model based on BEMD and MLPI with influencing factors

CCPortal

DOI	10.1016/j.atmosenv.2019.117200
	An enhanced interval PM2.5 concentration forecasting model based on BEMD and MLPI with influencing factors
	Wang Z.; Chen L.; Ding Z.; Chen H.
发表日期	2020
ISSN	1352-2310
卷号	223
英文摘要	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
英文关键词	Bivariate empirical mode decomposition; Interval forecasting; Interval multilayer perceptron; Mode reconstruction; PM2.5 concentration
语种	英语
scopus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
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/129636
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
推荐引用方式 GB/T 7714	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],2020,223.
APA	Wang Z.,Chen L.,Ding Z.,&Chen H..(2020).An enhanced interval PM2.5 concentration forecasting model based on BEMD and MLPI with influencing factors.Atmospheric Environment,223.
MLA	Wang Z.,et al."An enhanced interval PM2.5 concentration forecasting model based on BEMD and MLPI with influencing factors".Atmospheric Environment 223(2020).