Journal of Zhejiang University SCIENCE
(ISSN 1009-3095, Monthly)

2005   Vol. 6B   No. 6   p.491-495

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

QIN Zhong^†1, YU Qiang², LI Jun², WU Zhi-yi³, HU Bing-min^†4

(¹Institute of Ecology, School of Life Science, Zhejiang University, Hangzhou 310029, China)
(²Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)
(³Institute of Applied Entomology, School of Agriculture and Biotechnology, Zhejiang University, Hangzhou 310029, China)
(⁴School of Science, Zhejiang University, Hangzhou 310029, China)
^†E-mail: q_breeze@126.com; bmhu@mail.hz.zj.com
Received Oct. 7, 2004; revision accepted Feb. 28, 2005

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
doi:10.1631/jzus.2005.B0491             CLC number: S1; TP1.18