Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels
文献类型：期刊论文
| 作者 | Feng, Qi1; Wu, Shengjun2 ; Du, Yun1; Xue, Huaiping1; Xiao, Fei1; Ban, Xuan1; Li, Xiaodong1
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| 刊名 | ENVIRONMENTAL ENGINEERING SCIENCE
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| 出版日期 | 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 |
| WOS记录号 | WOS:000328881700003 |
| 出版者 | MARY ANN LIEBERT, INC |
| 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. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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