气候变化领域集成服务门户(CCPortal): Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels

CCPortal

DOI	10.1089/ees.2013.0164
	Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels
	Feng, Qi; Wu, Shengjun; Du, Yun; Xue, Huaiping; Xiao, Fei; Ban, Xuan; Li, Xiaodong
发表日期	2013
ISSN	1092-8758
EISSN	1557-9018
卷号	30 期号:12
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.
关键词	construction site fugitive dust neural network PM10 pollution
学科领域	Engineering; Environmental Sciences & Ecology
语种	英语
WOS研究方向	Engineering, Environmental ; Environmental Sciences
来源期刊	ENVIRONMENTAL ENGINEERING SCIENCE
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/111694
作者单位	Chinese Acad Sci, Inst Geodesy & Geophys, Key Lab Environm & Disaster Monitoring & Evaluat, Wuhan 430077, 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]. 中国科学院西北生态环境资源研究院,2013,30(12).
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).
MLA	Feng, Qi,et al."Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels".ENVIRONMENTAL ENGINEERING SCIENCE 30.12(2013).