Verifying Fossil-Fuel Carbon Dioxide Emissions Forecasted by an Artificial Neural Network with the GEOS-Chem Model
Verifying Fossil-Fuel Carbon Dioxide Emissions Forecasted by an Artificial Neural Network with the GEOS-Chem Model
复制标题
使用 GEOS-Chem 模型验证人工神经网络预测的化石燃料二氧化碳排放量
DOI：
10.3878/j.issn.1674-2834.14.0017
复制
发表时间：
2014-01
影响因子：
2.3
通讯作者：
PAN Yu-Bing
中科院分区：
文献类型：
--
作者：
WANG Yi-Nan;Lü Da-Ren;LI Qian;PAN Yu-Bing
摘要在这项研究中，作者开发了一个Elman神经网络集成来预测2009年化石燃料排放(Ff)的时空分布。基于不同地理区域的月平均网格排放数据(1979-2008)，建立并训练了29个Elman神经网络。应用戈达德地球观测系统(GEOS)-CHEM三维全球化学品输送模式，验证了网络的有效性。结果表明，该网络较好地反映了大气降水的年增长趋势和年际变化。在全球范围内，与原始和预测的ff之间的模拟差异在−1ppmv到1ppmv之间。同时，对观测和模拟的近地面大气CO2浓度的南北梯度进行了评估。这两个模拟的梯度似乎与观测结果有类似的变化模式，背景二氧化碳浓度略高，∼为1ppmv。结果表明，Elman神经网络是更好地了解大气CO2浓度和ff的时空分布的有用工具。
Abstract In this study, the authors developed an ensemble of Elman neural networks to forecast the spatial and temporal distribution of fossil-fuel emissions (ff) in 2009. The authors built and trained 29 Elman neural networks based on the monthly average grid emission data (1979–2008) from different geographical regions. A three-dimensional global chemical transport model, Goddard Earth Observing System (GEOS)-Chem, was applied to verify the effectiveness of the networks. The results showed that the networks captured the annual increasing trend and interannual variation of ff well. The difference between the simulations with the original and predicted ff ranged from −1 ppmv to 1 ppmv globally. Meanwhile, the authors evaluated the observed and simulated north-south gradient of the atmospheric CO2 concentrations near the surface. The two simulated gradients appeared to have a similar changing pattern to the observations, with a slightly higher background CO2 concentration, ∼ 1 ppmv. The results indicate that the Elman neural network is a useful tool for better understanding the spatial and temporal distribution of the atmospheric CO2 concentration and ff.
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影响因子：
4.4
作者：
Nassar, Ray;Napier-Linton, Louis;Deng, Feng
通讯作者：
Deng, Feng
影响因子：
3.1
作者：
P. Viotti;G. Liuti;P. Genova
通讯作者：
P. Viotti;G. Liuti;P. Genova
DOI：
10.1016/j.engappai.2004.02.002
发表时间：
2004-03-01
影响因子：
8
作者：
Niska, H;Hiltunen, T;Kolehmainen, M
通讯作者：
Kolehmainen, M
影响因子：
2.5
作者：
ELMAN, JL
通讯作者：
ELMAN, JL
影响因子：
6.3
作者：
Feng, L.;Palmer, P. I.;Dance, S.
通讯作者：
Dance, S.