DOI: 10.1016/j.atmosenv.2018.03.027
Scopus记录号: 2-s2.0-85046134663
论文题名: Artificial neural network model for ozone concentration estimation and Monte Carlo analysis
作者: Gao M ; , Yin L ; , Ning J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 184 起始页码: 129
结束页码: 139
语种: 英语
英文关键词: Air pollution
; Artificial neural network
; Monte Carlo simulation
; Sensitivity analysis
; Uncertainty analysis
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations. © 2018 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82846
Appears in Collections: 气候变化事实与影响
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作者单位: Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, China; University of Chinese Academy of Sciences, Beijing, China
Recommended Citation:
Gao M,, Yin L,, Ning J. Artificial neural network model for ozone concentration estimation and Monte Carlo analysis[J]. Atmospheric Environment,2018-01-01,184