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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Project supported by the National Science Fund for Outstanding Youth Overseas (No. 40328001) and the Key Research Plan of the Knowledge Innovation Project of the Institute of Geographic Sciences and Natural Reseources, Chinese Academy of Sciences (No. KZCXI-SW-01)
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Qin, Z., Yu, Q., Li, J. et al. Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland. J Zheijang Univ Sci B 6, 491–495 (2005). https://doi.org/10.1631/jzus.2005.B0491
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DOI: https://doi.org/10.1631/jzus.2005.B0491
Key words
- Least squares support vector machines (LS-SVMs)
- Water vapor and carbon dioxide fluxes exchange
- Radial basis function (RBF) neural networks