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

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dc.contributor.author Qin, Z
dc.contributor.author Yu, Q
dc.contributor.author Li, J
dc.contributor.author Wu, Z
dc.contributor.author Hu, B
dc.date.accessioned 2010-05-28T09:44:58Z
dc.date.issued 2005-01
dc.identifier.citation Journal of Zhejiang University Science, 2005, 6B (6), pp. 491 - 495
dc.identifier.issn 1009-3095
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/8709
dc.description.abstract Least squares support vector machines (LS-SVMs), a nonlinear kemel 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.
dc.publisher Zheijiang University Press
dc.subject Least squares support vector machines (LS-SVMs), Water vapor and carbon dioxide fluxes exchange, Radial basis function (RBF) neural networks
dc.subject Least squares support vector machines (LS-SVMs), Water vapor and carbon dioxide fluxes exchange, Radial basis function (RBF) neural networks
dc.title Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland
dc.type Journal Article
dc.parent Journal of Zhejiang University Science
dc.journal.volume 6
dc.journal.volume 6B
dc.journal.number 6 en_US
dc.publocation China en_US
dc.identifier.startpage 491 en_US
dc.identifier.endpage 495 en_US
dc.cauo.name SCI.Faculty of Science en_US
dc.conference Verified OK en_US
dc.for 1001 Agricultural Biotechnology
dc.personcode 0000051731 en_US
dc.personcode 107001 en_US
dc.personcode 0000071175 en_US
dc.personcode 0000052150 en_US
dc.personcode 0000052128 en_US
dc.percentage 100 en_US
dc.classification.name Agricultural Biotechnology en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Least squares support vector machines (LS-SVMs), Water vapor and carbon dioxide fluxes exchange, Radial basis function (RBF) neural networks en_US
dc.description.keywords Least squares support vector machines (LS-SVMs), Water vapor and carbon dioxide fluxes exchange, Radial basis function (RBF) neural networks
dc.staffid en_US
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
pubs.organisational-group /University of Technology Sydney/Strength - C3


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