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Journal of Zhejiang University SCIENCE B

ISSN 1673-1581(Print), 1862-1783(Online), Monthly

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

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.

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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DOI:

10.1631/jzus.2005.B0491

CLC number:

S1; TP1.18

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Received:

2004-10-07

Revision Accepted:

2005-02-28

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