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Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels
Feng, Qi1; Wu, Shengjun2; Du, Yun1; Xue, Huaiping1; Xiao, Fei1; Ban, Xuan1; Li, Xiaodong1
2013-12-01
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
DOI10.1089/ees.2013.0164
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发表期刊ENVIRONMENTAL ENGINEERING SCIENCE
ISSN1092-8758
卷号30期号:12页码:725-732
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.
收录类别SCI
WOS记录号WOS:000328881700003
语种英语