Document Detail

Modeling water and carbon fluxes above summer maize field in North China Plain with back-propagation neural networks.
MedLine Citation:
PMID:  15822158     Owner:  NLM     Status:  MEDLINE    
In this work, datasets of water and carbon fluxes measured with eddy covariance technique above a summer maize field in the North China Plain were simulated with artificial neural networks (ANNs) to explore the fluxes responses to local environmental variables. The results showed that photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T) and leaf area index (LAI) were primary factors regulating both water vapor and carbon dioxide fluxes. Three-layer back-propagation neural networks (BP) could be applied to model fluxes exchange between cropland surface and atmosphere without using detailed physiological information or specific parameters of the plant.
Zhong Qin; Gao-Li Su; Qiang Yu; Bing-Min Hu; Jun Li
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Journal of Zhejiang University. Science. B     Volume:  6     ISSN:  1673-1581     ISO Abbreviation:  -     Publication Date:  2005 May 
Date Detail:
Created Date:  2005-04-11     Completed Date:  2005-09-30     Revised Date:  2008-11-20    
Medline Journal Info:
Nlm Unique ID:  101236535     Medline TA:  J Zhejiang Univ Sci B     Country:  China    
Other Details:
Languages:  eng     Pagination:  418-26     Citation Subset:  IM    
Ecology academy, School of Life Science, Zhejiang University, Hangzhou 310029, China;
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MeSH Terms
Carbon / metabolism*
Carbon Dioxide / metabolism
Models, Biological*
Neural Networks (Computer)*
Water / metabolism*
Zea mays / metabolism*
Reg. No./Substance:
124-38-9/Carbon Dioxide; 7440-44-0/Carbon; 7732-18-5/Water

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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