Autoencoder-based deep belief regression network for air particulate matter concentration forecasting

doi:10.3233/JIFS-169527

IHEP OpenIR > 东莞研究部

	Autoencoder-based deep belief regression network for air particulate matter concentration forecasting
	Xie, JJ; Wang, XX; 刘宇(东); Liu, Y; Bai, Y
	2018
发表期刊	JOURNAL OF INTELLIGENT & FUZZY SYSTEMS (IF:1.261[JCR-2016],1.284[5-Year])
ISSN	1064-1246
EISSN	1875-8967
卷号	34 期号:6 页码:3475-3486
文章类型	Article
摘要	Particulate matter (PM) is one of the most significant air pollutants in recent decades that has tremendous negative effects on the ambient air quality and the public health. Accurate PM forecasting provides a possibility for establishing an early warning system. In this paper, a deep feature learning architecture, i.e., autoencoder-based deep belief regression network (AE-based DBRN), is introduced and utilized to forecast the daily PM concentrations (PM2.5 and PM10). Prior to establishing this model, Pearson correlation analysis is applied to look for the possible input-output mapping, where the input candidate variables contain seven meteorological parameters and PM concentrations within one-day ahead, and the output variables are the local PM forecasts. The addressed model was evaluated by the dataset in the period of 28/10/2013 to 31/8/2016 in Chongqing municipality of China. Moreover, two shallow models, feed forward neural network and least squares support vector regression, were employed for the comparison. The results indicate that the AE-based DBRN model has remarkable better performances among the comparison models in terms of mean absolute percentage error (PM2.5 21.092%, PM10 19.474%), root mean square error (PM2.5 8.600 mu g/m(3), PM10 11.239 mu g/m(3)) and correlation coefficient criteria (PM2.5 0.840, PM10 0.826).
关键词	Deep belief regression network autoencoder particulate matter meteorological data forecasting
DOI	10.3233/JIFS-169527
关键词[WOS]	NEURAL-NETWORK ; MODEL ; PREDICTION ; POLLUTION ; ROADSIDE
收录类别	SCI ; EI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000436432400008
EI入藏号	20182805521951
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://ir.ihep.ac.cn/handle/311005/286064
专题	东莞研究部实验物理中心
作者单位	中国科学院高能物理研究所
第一作者单位	中国科学院高能物理研究所
推荐引用方式 GB/T 7714	Xie, JJ,Wang, XX,Liu Y,et al. Autoencoder-based deep belief regression network for air particulate matter concentration forecasting[J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,2018,34(6):3475-3486.
APA	Xie, JJ,Wang, XX,刘宇,Liu, Y,&Bai, Y.(2018).Autoencoder-based deep belief regression network for air particulate matter concentration forecasting.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,34(6),3475-3486.
MLA	Xie, JJ,et al."Autoencoder-based deep belief regression network for air particulate matter concentration forecasting".JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 34.6(2018):3475-3486.