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.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 107001
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.description.keywords Least squares support vector machines (LS-SVMs), Water vapor and carbon dioxide fluxes exchange, Radial basis function (RBF) neural networks
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
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10


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