Gao, Meng
; Yin, Liting; Ning, Jicai发表期刊ATMOSPHERIC ENVIRONMENT
ISSN1352-2310
2018-07
卷号184页码:129-139
关键词Air pollutionArtificial neural networkMonte Carlo simulationUncertainty analysisSensitivity analysis
研究领域Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences
DOI10.1016/j.atmosenv.2018.03.027
产权排序[Gao, Meng; Yin, Liting; Ning, Jicai] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China; [Yin, Liting] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
作者部门海岸带信息集成与综合管理实验室
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.
文章类型Article
资助机构Youth Innovation Promotion Association of CAS [2016195]; CAS Knowledge Innovation Project [KZCX2-EW-QN209]; National Natural Science Foundation of China [31570423]
收录类别SCI
语种英语
关键词[WOS]UNCERTAINTY ANALYSIS; TROPOSPHERIC OZONE; REGRESSION-MODELS; PREDICTION; SIMULATION; PRECIPITATION; TRENDS; CHINA; AIR; UK
研究领域[WOS]Environmental Sciences; Meteorology & Atmospheric Sciences
WOS记录号WOS:000433652300014
引用统计被引频次：38[WOS][WOS记录][WOS相关记录]
文献类型期刊论文
条目标识符http://ir.yic.ac.cnhttp://ir.yic.ac.cn/handle/133337/24447
专题中科院海岸带环境过程与生态修复重点实验室_海岸带信息集成与战略规划研究中心
中科院海岸带环境过程与生态修复重点实验室
作者单位1.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China;
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714Gao, Meng,Yin, Liting,Ning, Jicai. Artificial neural network model for ozone concentration estimation and Monte Carlo analysis[J]. ATMOSPHERIC ENVIRONMENT,2018,184:129-139.
APAGao, Meng,Yin, Liting,&Ning, Jicai.(2018).Artificial neural network model for ozone concentration estimation and Monte Carlo analysis.ATMOSPHERIC ENVIRONMENT,184,129-139.
MLAGao, Meng,et al."Artificial neural network model for ozone concentration estimation and Monte Carlo analysis".ATMOSPHERIC ENVIRONMENT 184(2018):129-139.
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