CLC number: S1; TP1.18
On-line Access:
Received: 2004-10-07
Revision Accepted: 2005-02-28
Crosschecked: 0000-00-00
Cited: 4
Clicked: 7275
QIN Zhong, YU Qiang, LI Jun, WU Zhi-yi, HU Bing-min. Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland[J]. Journal of Zhejiang University Science B, 2005, 6(6): 491-495.
@article{title="Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland",
author="QIN Zhong, YU Qiang, LI Jun, WU Zhi-yi, HU Bing-min",
journal="Journal of Zhejiang University Science B",
volume="6",
number="6",
pages="491-495",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.B0491"
}
%0 Journal Article
%T Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland
%A QIN Zhong
%A YU Qiang
%A LI Jun
%A WU Zhi-yi
%A HU Bing-min
%J Journal of Zhejiang University SCIENCE B
%V 6
%N 6
%P 491-495
%@ 1673-1581
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.B0491
TY - JOUR
T1 - Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland
A1 - QIN Zhong
A1 - YU Qiang
A1 - LI Jun
A1 - WU Zhi-yi
A1 - HU Bing-min
J0 - Journal of Zhejiang University Science B
VL - 6
IS - 6
SP - 491
EP - 495
%@ 1673-1581
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.B0491
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.
[1] Anthoni, P.M., Freibauer, A., Kolle, O., Schulze, E.D., 2004. Winter wheat carbon exchange in Thuringia, Germany. Agricultural and Forest Meteorology, 121:55-67.
[2] Arora, V.K., 2003. Simulating energy and carbon fluxes over winter wheat using coupled land surface and terrestrial ecosystem models. Agricultural and Forest Meteorology, 118:21-47.
[3] Baldocchi, D.D., Wilson, K.B., 2001. Modelling CO2 and water vapor exchange of a temperate broadleaved forest across hourly to decadal time scales. Ecological Modelling, 142:155-184.
[4] Bosveld, F.C., Bouten, W., 2001. Evaluation of transpiration models with observations over a Douglas-fir forest. Agricultural and Forest Meteorology, 108:247-264.
[5] Demuth, H., Beale, M., 1994. Neural Network Toolbox for Use with MATLAB. Natick, The MathWorks, Inc.
[6] Duan, K., Keerthi, S., Poo, A., 2001. Evaluation of Simple Performance Measures for Tuning SVM Hyperparameters (Tech. Rep. No. Control Division Technical Report CD-01-11). Department of Mechanical Engineering, National University of Singapore.
[7] Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G., Ceulemans, R., Clement, R., Dolman, H., et al., 2001. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology, 107:43-69.
[8] Flerchinger, G.N., Pierson, F.B., 1991. Modelling plant canopy effects on variability of soil temperature and water. Agric. and Forest Meteor., 56:227-246.
[9] Flerchinger, G.N., Hanson, C.L., Wight, J.R., 1996. Modelling of evapotranspiration and surface energy budgets across a watershed. Water Resour. Res., 32(8):2539-2548.
[10] Franks, S.W., Beven, K., 1999. Conditioning a multiple patch SVAT model using uncertain time-space estimates of latent heat fluxes as inferred from remotely sensed data. Water Resour. Res., 35:2751-2761.
[11] Franks, S.W., Beven, K.J., Quinn, P.F., Wright, I.R., 1997. On the sensitivity of soil-vegetation-atmosphere transfer (SVAT) schemes: Equifinality and the problem of robust calibration. Agric. Forest Meteor., 86:63-75.
[12] Granier, A., Ceschia, E., Damesin, C., Dufrêne, E., Epron, D., Gross, P., Lebaube, S., Le Dantec, V., Le Goff, N., Lemoine, D., et al., 2000a. The carbon balance of a young beech forest. Funct. Ecol., 14:312-325.
[13] Granier, A., Biron, P., Lemoine, D., 2000b. Water balance, transpiration and canopy conductance in two beech stands. Agric. Forest Meteor., 100:291-308.
[14] Huntingford, C., Cox, P.M., 1997. Use of statistical and neural network techniques to detect how stomatal conductance responds to changes in the local environment. Ecol. Model., 97:217-246.
[15] Jemwa, G.T., Aldrich, C., 2003. Identification of Chaotic Process Systems with Least Squares Support Vector Machines. Neural Networks. Proceedings of the International Joint Conference on Volume 3, p.20-24.
[16] Kelliher, F.M., Hollinger, D.Y., Schulze, E.D., Vygodskaya, N.N., Byers, J.N., Hunt, J.E., McSeveny, T.M., Milukova, I., Sogachev, A.F., Varlagin, A.V., et al., 1997. Evaporation from an eastern Siberian larch forest. Agric. Forest Meteor., 85:135-147.
[17] Kosko, B., 1992. Neural Networks and Fuzzy Systems. A Dynamical Systems Approach to Machine Intelligence. New Jersey, Prentice-Hall, Inc, Englewood Cliffs, p.449.
[18] Mo, X.G., Beven, K., 2004. Multi-objective parameter conditioning of a three-source wheat canopy model. Agricultural and Forest Meteorology, 122:39-63.
[19] Pelckmans, K., Suykens, J.A.K., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., Moor, B., Vandewalle, J., 2002. A Matlab/C Toolbox for Least Squares Support Vector Machines. Internal Report 02-44, ESAT-SISTA and K.U. Leuven, Belgium.
[20] Schulz, H., Härtling, S., 2003. Vitality analysis of Scots pines using a multivariate approach. Forest Ecology and Management, 186:73-846.
[21] Suykens, J.A.K., 2001. Nonlinear Modelling and Support Vector Machines. Budapest, Hungary. IEEE Instruments and Measurement Technology Conference.
[22] Thissen, U., van Brakel, R., de Weijer, A.P., Melssen, W.J., Buydens, L.M.C., 2003. Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems, 69:35-49.
[23] Unland, H.E., Houser, P.R., Shuttleworth, W.J., Yang, Z.L., 1996. Surface flux measurement and modelling at a semi-arid Sonoran Desert site. Agricultural and Forest Meteorology, 82:119-153.
[24] Valentini, R., Deangelis, P., Matteucci, G., Monaco, R., Dore, S., Mugnozza, G.E.S., 1996. Seasonal net carbon dioxide exchange of a beech forest with the atmosphere. Global Change Biol., 2:199-208.
[25] Van Wijk, M.T., Bouten. W., 1999. Water and carbon fluxes above European coniferous forests modeled with artificial neural networks. Ecological Modelling, 20:181-197.
[26] Vapnik, V., 1995. The Nature of Statistical Learning Theory. Sringer-Verlag, New York, p.311.
[27] Vapnik, V., 1998. Statistical Learning Theory. John Wiley and Sons, New York.
[28] Vapnik, V., 1999. The Nature of Statistical Learning Theory. 2nd Ed., Springer-Verlag, New York.
[29] Witten, I. H., Frank, E., 2000. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. San Diego, CA: Morgan Kaufmann.
Open peer comments: Debate/Discuss/Question/Opinion
<1>