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Chinese Academy of Sciences Institutional Repositories Grid
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  2. 烟台海岸带研究所
  3. 中国科学院烟台海岸带研究所
  4. 中科院海岸带环境过程与生态修复重点实验室
  5. 海岸带信息集成与综合管理实验室
Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

文献类型：期刊论文

作者Gao, Meng; Yin, Liting; Ning, Jicai
刊名ATMOSPHERIC ENVIRONMENT
出版日期2018-07
卷号184页码:129-139
关键词Air pollution Artificial neural network Monte Carlo simulation Uncertainty analysis Sensitivity analysis
ISSN号1352-2310
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
文献子类Article
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.
WOS关键词UNCERTAINTY ANALYSIS ; TROPOSPHERIC OZONE ; REGRESSION-MODELS ; PREDICTION ; SIMULATION ; PRECIPITATION ; TRENDS ; CHINA ; AIR ; UK
WOS研究方向Environmental Sciences ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000433652300014
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]
源URL[http://ir.yic.ac.cn/handle/133337/24447]  
专题烟台海岸带研究所_海岸带信息集成与综合管理实验室
烟台海岸带研究所_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 7714		 Gao, Meng,Yin, Liting,Ning, Jicai. Artificial neural network model for ozone concentration estimation and Monte Carlo analysis[J]. ATMOSPHERIC ENVIRONMENT,2018,184:129-139.
APA Gao, Meng,Yin, Liting,&Ning, Jicai.(2018).Artificial neural network model for ozone concentration estimation and Monte Carlo analysis.ATMOSPHERIC ENVIRONMENT,184,129-139.
MLA Gao, Meng,et al."Artificial neural network model for ozone concentration estimation and Monte Carlo analysis".ATMOSPHERIC ENVIRONMENT 184(2018):129-139.

入库方式： OAI收割

来源：烟台海岸带研究所

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