Artificial neural network model for ozone concentration estimation and Monte Carlo analysis | |
Gao, Meng; Yin, Liting; Ning, Jicai | |
发表期刊 | ATMOSPHERIC ENVIRONMENT |
ISSN | 1352-2310 |
2018-07 | |
卷号 | 184页码:129-139 |
关键词 | Air pollution Artificial neural network Monte Carlo simulation Uncertainty analysis Sensitivity analysis |
研究领域 | Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences |
DOI | 10.1016/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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.yic.ac.cn/handle/133337/24447 |
专题 | 中国科学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 中国科学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. |
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