Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels

doi:10.1089/ees.2013.0164

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 生态过程与重建研究中心

	Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels
	Feng, Qi 1; Wu, Shengjun2 ; Du, Yun 1; Xue, Huaiping 1; Xiao, Fei 1; Ban, Xuan 1; Li, Xiaodong 1
刊名	ENVIRONMENTAL ENGINEERING SCIENCE
	2013-12-01
卷号	30 期号:12 页码:725-732
关键词	construction site fugitive dust neural network PM10 pollution
ISSN号	1092-8758
DOI	10.1089/ees.2013.0164
通讯作者	Feng, Q (reprint author), Chinese Acad Sci, Inst Geodesy & Geophys, Key Lab Environm & Disaster Monitoring & Evaluat, 340 Xudong Rd, Wuhan 430077, Peoples R China.
英文摘要	Fugitive dust deriving from construction sites is a serious local source of particulate matter (PM) that leads to air pollution in cities undergoing rapid urbanization in China. In spite of this fact, no study has yet been published relating to prediction of high levels of PM with diameters <10m (PM10) as adjudicated by the Individual Air Quality Index (IAQI) on fugitive dust from nearby construction sites. To combat this problem, the Construction Influence Index (Ci) is introduced in this article to improve forecasting models based on three neural network models (multilayer perceptron, Elman, and support vector machine) in predicting daily PM10 IAQI one day in advance. To obtain acceptable forecasting accuracy, measured time series data were decomposed into wavelet representations and wavelet coefficients were predicted. Effectiveness of these forecasters were tested using a time series recorded between January 1, 2005, and December 31, 2011, at six monitoring stations situated within the urban area of the city of Wuhan, China. Experimental trials showed that the improved models provided low root mean square error values and mean absolute error values in comparison to the original models. In addition, these improved models resulted in higher values of coefficients of determination and AHPC (the accuracy rate of high PM10 IAQI caused by nearby construction activity) compared to the original models when predicting high PM10 IAQI levels attributable to fugitive dust from nearby construction sites.
URL标识	查看原文
资助项目	National Natural Science Foundation of China[41301098] ; National Natural Science Foundation of China[41271125] ; National Natural Science Foundation of China[51109195]
WOS研究方向	Engineering ; Environmental Sciences & Ecology
语种	英语
出版者	MARY ANN LIEBERT, INC
WOS记录号	WOS:000328881700003
内容类型	期刊论文
源URL	[http://119.78.100.138/handle/2HOD01W0/466]
专题	生态过程与重建研究中心
通讯作者	Feng, Qi
作者单位	1.Chinese Acad Sci, Inst Geodesy & Geophys, Key Lab Environm & Disaster Monitoring & Evaluat, Wuhan 430077, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing, Peoples R China
推荐引用方式 GB/T 7714	Feng, Qi,Wu, Shengjun,Du, Yun,et al. Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels[J]. ENVIRONMENTAL ENGINEERING SCIENCE,2013,30(12):725-732.
APA	Feng, Qi.,Wu, Shengjun.,Du, Yun.,Xue, Huaiping.,Xiao, Fei.,...&Li, Xiaodong.(2013).Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels.ENVIRONMENTAL ENGINEERING SCIENCE,30(12),725-732.
MLA	Feng, Qi,et al."Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels".ENVIRONMENTAL ENGINEERING SCIENCE 30.12(2013):725-732.