Modeling water and carbon fluxes above summer maize field in North China Plain with back-propagation neural networks

Qin, Z; Su, G; Yu, Q; Hu, B; Li, J

Modeling water and carbon fluxes above summer maize field in North China Plain with back-propagation neural networks

Qin, Z Su, G Yu, Q

Hu, B Li, J

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Publisher:: Zheijiang University Press
Publication Type:: Journal Article
Citation:: Journal of Zhejiang University Science, 2005, 6B (5), pp. 418 - 426
Issue Date:: 2005-01

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Field	Value	Language
dc.contributor.author	Qin, Z	en_US
dc.contributor.author	Su, G	en_US
dc.contributor.author	Yu, Q https://orcid.org/0000-0001-6950-1821	en_US
dc.contributor.author	Hu, B	en_US
dc.contributor.author	Li, J	en_US
dc.date.issued	2005-01	en_US
dc.identifier.citation	Journal of Zhejiang University Science, 2005, 6B (5), pp. 418 - 426	en_US
dc.identifier.issn	1009-3095	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8809
dc.description.abstract	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.	en_US
dc.publisher	Zheijiang University Press	en_US
dc.relation.ispartof	Journal of Zhejiang University Science	en_US
dc.subject.classification	Plant Biology & Botany	en_US
dc.title	Modeling water and carbon fluxes above summer maize field in North China Plain with back-propagation neural networks	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	6B	en_US
utslib.for	0699 Other Biological Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	10 Technology	en_US
utslib.for	11 Medical and Health Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	5	en_US
pubs.volume	6B	en_US

Abstract:

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.

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http://hdl.handle.net/10453/8809