Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland |
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| Authors: | Qin Zhong Yu Qiang Li Jun Wu Zhi-yi Hu Bing-min |
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| Institution: | Institute of Ecology, School of Life Science, Zhejiang University, Hangzhou 310029, China. q_breeze@126.com |
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| 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. |
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| Keywords: | Least squares support vector machines (LS-SVMs) Water vapor and carbon dioxide fluxes exchange Radial basis function (RBF) neural networks |
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