title: Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland.
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.
authors.affiliation: Institute of Ecology, School of Life Science, Zhejiang University, Hangzhou 310029, China. q_breeze@126.com
authors.forename: Zhong
authors.initials: Z
authors.lastname: Qin
authors.forename: Qiang
authors.initials: Q
authors.lastname: Yu
authors.forename: Jun
authors.initials: J
authors.lastname: Li
authors.forename: Zhi-yi
authors.initials: ZY
authors.lastname: Wu
authors.forename: Bing-min
authors.initials: BM
authors.lastname: Hu
mesh_terms: D000383:Agriculture / Q000379:methods; D001185:Artificial Intelligence; D001272:Atmosphere / Q000737:chemistry*; D002245:Carbon Dioxide / Q000032:analysis / Q000737:chemistry* / Q000378:metabolism*; D003198:Computer Simulation; D018556:Crops, Agricultural / Q000502:physiology*; D017753:Ecosystem; D016018:Least-Squares Analysis; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D014867:Water / Q000032:analysis / Q000737:chemistry* / Q000378:metabolism*
chemical_list: D014867:Water; D002245:Carbon Dioxide