| Biotechnology |
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| Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland |
| QIN Zhong, YU Qiang, LI Jun, WU Zhi-yi, HU Bing-min |
| Institute of Ecology, School of Life Science, Zhejiang University, Hangzhou 310029, China; Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Institute of Applied Entomology, School of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310029, China; School of Science, Zhejiang University, Hangzhou 310029, China |
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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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Received: 07 October 2004
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