Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland.
- Author:
Zhong QIN
1
;
Qiang YU
;
Jun LI
;
Zhi-yi WU
;
Bing-min HU
Author Information
1. Institute of Ecology, School of Life Science, Zhejiang University, Hangzhou 310029, China. q_breeze@126.com
- Publication Type:Journal Article
- MeSH:
Agriculture;
methods;
Artificial Intelligence;
Atmosphere;
chemistry;
Carbon Dioxide;
analysis;
chemistry;
metabolism;
Computer Simulation;
Crops, Agricultural;
physiology;
Ecosystem;
Least-Squares Analysis;
Models, Biological;
Models, Chemical;
Models, Statistical;
Water;
analysis;
chemistry;
metabolism
- From:
Journal of Zhejiang University. Science. B
2005;6(6):491-495
- CountryChina
- Language:English
-
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.
