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Received: 2004-10-07

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Journal of Zhejiang University SCIENCE B 2005 Vol.6 No.6 P.491~495

doi: 10.1631/jzus.2005.B0491

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

Author(s):  QIN Zhong, YU Qiang, LI Jun, WU Zhi-yi, HU Bing-min

Affiliation(s):  Institute of Ecology, School of Life Science, Zhejiang University, Hangzhou 310029, China; more

Corresponding email(s):  q_breeze@126.com, bmhu@mail.hz.zj.com

Key Words:  Least squares support vector machines (LS-SVMs), Water vapor and carbon dioxide fluxes exchange, Radial basis function (RBF) neural networks

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QIN Zhong, YU Qiang, LI Jun, WU Zhi-yi, HU Bing-min. Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland[J]. Journal of Zhejiang University Science B, 2005, 6(8): 491~495.

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publisher="Zhejiang University Press & Springer",

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T1 - Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland
A1 - QIN Zhong
A1 - YU Qiang
A1 - LI Jun
A1 - WU Zhi-yi
A1 - HU Bing-min
J0 - Journal of Zhejiang University Science B
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SP - 491
EP - 495
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PB - Zhejiang University Press & Springer
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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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