Artificial Neural Network Models for Daily PM(10) Air Pollution Index Prediction in the Urban Area of Wuhan, China | |
Wu, Shengjun ; Feng, Qi ; Du, Yun ; Li, Xiaodong | |
刊名 | Environmental Engineering Science |
2011 | |
期号 | 5页码:357-363 |
中文摘要 | Dust storm is a critical remote source that causes low air quality in many cities in China. The prediction accuracy of high particulate matter with a diameter <10 mu m(PM(10)) air pollution index (API) event caused by dust storm is low in China. To solve this problem, dust storm data from northern China was first used to tune the Elman-based forecast model to predict the daily PM(10) API with a lead time of 1 day. Effectiveness of this forecaster was tested using a time series recorded from September 1, 2001, to December 31, 2007, at six monitoring stations located within the urban area of Wuhan, China. Experimental trials show that the improved Elman model provides low root mean square error values and mean absolute error values in comparison to the standard Elman model. In addition, higher coefficient of determination (r(2) = 0.62) and accuracy rate (83.33%) values were realized for the improved Elman model in comparison to the standard Elman model (r(2) = 0.22, accuracy rate = 64.81%) when predicting high PM(10) API events caused by dust storms. |
原文出处 | |
公开日期 | 2012-04-11 |
内容类型 | 期刊论文 |
源URL | [http://ir.whigg.ac.cn/handle/342008/3368] |
专题 | 测量与地球物理研究所_其他_期刊论文 |
推荐引用方式 GB/T 7714 | Wu, Shengjun,Feng, Qi,Du, Yun,et al. Artificial Neural Network Models for Daily PM(10) Air Pollution Index Prediction in the Urban Area of Wuhan, China[J]. Environmental Engineering Science,2011(5):357-363. |
APA | Wu, Shengjun,Feng, Qi,Du, Yun,&Li, Xiaodong.(2011).Artificial Neural Network Models for Daily PM(10) Air Pollution Index Prediction in the Urban Area of Wuhan, China.Environmental Engineering Science(5),357-363. |
MLA | Wu, Shengjun,et al."Artificial Neural Network Models for Daily PM(10) Air Pollution Index Prediction in the Urban Area of Wuhan, China".Environmental Engineering Science .5(2011):357-363. |
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