| DOI: | 10.1016/j.atmosenv.2014.02.046
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| Scopus记录号: | 2-s2.0-84895991196
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| 论文题名: | Near-term projection of anthropogenic emission trends using neural networks |
| 作者: | Balsamà A; P; , De Biase L; , Janssens-Maenhout G; , Pagliari V
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| 刊名: | Atmospheric Environment
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| ISSN: | 0168-2563
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| EISSN: | 1573-515X
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| 出版年: | 2014
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| 卷: | 89 | | 起始页码: | 581
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| 结束页码: | 592
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| 语种: | 英语
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| 英文关键词: | Neural networks
; Prediction
; Projection
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| Scopus关键词: | Carbon dioxide
; Chemical analysis
; Database systems
; Forecasting
; Principal component analysis
; Sulfur dioxide
; Anthropogenic emissions
; Atmospheric research
; Chemical species
; Chemical substance
; Emission trends
; General regression neural networks (GRNNs)
; Nonlinear behaviours
; Projection
; Neural networks
; carbon dioxide
; nitrous oxide
; sulfur dioxide
; anthropogenic source
; artificial neural network
; atmospheric modeling
; carbon dioxide
; chemical composition
; climate prediction
; database
; Gaussian method
; methane
; principal component analysis
; sulfur dioxide
; time series
; analytic method
; anthropogenic emission trend
; article
; controlled study
; environmental aspects and related phenomena
; environmental parameters
; general regression neural network
; multi layers perception
; normal distribution
; prediction
; principal component analysis
; priority journal
; time series analysis
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| Scopus学科分类: | Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
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| 英文摘要: | This study is the first ever analysis of the global time series 1970-2008 of the Emissions Database for Global Atmospheric Research (EDGAR) for 10 chemical species and more than 3000 subsectors with neural networks, which tries to find non-linear behaviours that several species have in common.The application of the different neural network types, suggests that General Regression Neural Networks (GRNNs) are the most suitable to train a typical Gaussian trend with a very low error level. As such, GRNNs are very suitable for filling the data points missing from the EDGAR time-series, but they are not so good at a making projections outside the time period of the database. Instead Multi Layers Perceptron (MLP) is very suitable for projecting a subsequent year to the database time period of several decades, even though MLP is characterised by a slightly higher absolute mean error than the GRNN.By means of the Principal Component Analysis (PCA), we identified which chemical substances are driven similarly by the activity data over the almost 40 years time period. In all the geographic aggregations, we observed that the emission trends of CO2, SO2 and NOx can be grouped into one cluster, and the emission trends of CH4 and the particulates into another. The best time interval for the prediction proved to be eleven years, and projections seemed to be reliable for three consecutive years following the last year of the database time-series. © 2014. |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/81082
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| Appears in Collections: | 气候变化事实与影响
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作者单位: | Dept. of Environmental Sciences, University of Milano-Bicocca, Italy; Joint Research Centre, European Commission, 21027 Ispra, Italy
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| Recommended Citation: | |
Balsamà A,P,, De Biase L,et al. Near-term projection of anthropogenic emission trends using neural networks[J]. Atmospheric Environment,2014-01-01,89
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