Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland

Qin, Z; Yu, Q; Li, J; Wu, ZY; Hu, BM

Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland

Qin, Z Yu, Q

Li, J Wu, ZY Hu, BM

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Publication Type:: Journal Article
Citation:: Journal of Zhejiang University: Science, 2005, 6 B (6), pp. 491 - 495
Issue Date:: 2005-06-01

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Field	Value	Language
dc.contributor.author	Qin, Z	en_US
dc.contributor.author	Yu, Q https://orcid.org/0000-0001-6950-1821	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	Wu, ZY	en_US
dc.contributor.author	Hu, BM	en_US
dc.date.issued	2005-06-01	en_US
dc.identifier.citation	Journal of Zhejiang University: Science, 2005, 6 B (6), pp. 491 - 495	en_US
dc.identifier.issn	1009-3095	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8709
dc.description.abstract	Least squares support vector machines (LS-SVMs), a nonlinear kernel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obtained with radial basis function (RBF) neural networks. The results indicated the trained LS-SVMs with a radial basis function kernel had satisfactory performance in modelling surface fluxes; its excellent approximation and generalization property shed new light on the study on complex processes in ecosystem.	en_US
dc.relation.ispartof	Journal of Zhejiang University: Science	en_US
dc.relation.isbasedon	10.1631/jzus.2005.B0491	en_US
dc.subject.classification	Plant Biology & Botany	en_US
dc.title	Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	6 B	en_US
utslib.for	1001 Agricultural Biotechnology	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.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	6 B	en_US

Abstract:

Least squares support vector machines (LS-SVMs), a nonlinear kernel based machine was introduced to investigate the prospects of application of this approach in modelling water vapor and carbon dioxide fluxes above a summer maize field using the dataset obtained in the North China Plain with eddy covariance technique. The performances of the LS-SVMs were compared to the corresponding models obtained with radial basis function (RBF) neural networks. The results indicated the trained LS-SVMs with a radial basis function kernel had satisfactory performance in modelling surface fluxes; its excellent approximation and generalization property shed new light on the study on complex processes in ecosystem.

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http://hdl.handle.net/10453/8709