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

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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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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