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Received: 2004-08-03

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Journal of Zhejiang University SCIENCE B 2005 Vol.6 No.5 P.418-426


Modeling water and carbon fluxes above summer maize field in North China Plain with Back-propagation neural networks

Author(s):  QIN Zhong, SU Gao-li, YU Qiang, HU Bing-min, LI Jun

Affiliation(s):  Ecology academy, School of Life Science, Zhejiang University, Hangzhou 310029, China; more

Corresponding email(s):   q_breeze@126.com, bmhu@mail.hz.zj.com

Key Words:  Carbon dioxide, Water vapor and heat fluxes, Three-layer back-propagation neural networks

QIN Zhong, SU Gao-li, YU Qiang, HU Bing-min, LI Jun. Modeling water and carbon fluxes above summer maize field in North China Plain with Back-propagation neural networks[J]. Journal of Zhejiang University Science B, 2005, 6(5): 418-426.

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author="QIN Zhong, SU Gao-li, YU Qiang, HU Bing-min, LI Jun",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Modeling water and carbon fluxes above summer maize field in North China Plain with Back-propagation neural networks
%A QIN Zhong
%A SU Gao-li
%A YU Qiang
%A HU Bing-min
%A LI Jun
%J Journal of Zhejiang University SCIENCE B
%V 6
%N 5
%P 418-426
%@ 1673-1581
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.B0418

T1 - Modeling water and carbon fluxes above summer maize field in North China Plain with Back-propagation neural networks
A1 - QIN Zhong
A1 - SU Gao-li
A1 - YU Qiang
A1 - HU Bing-min
A1 - LI Jun
J0 - Journal of Zhejiang University Science B
VL - 6
IS - 5
SP - 418
EP - 426
%@ 1673-1581
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.B0418

In this work, datasets of water and carbon fluxes measured with eddy covariance technique above a summer maize field in the North China Plain were simulated with artificial neural networks (ANNs) to explore the fluxes responses to local environmental variables. The results showed that photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T) and leaf area index (LAI) were primary factors regulating both water vapor and carbon dioxide fluxes. three-layer back-propagation neural networks (BP) could be applied to model fluxes exchange between cropland surface and atmosphere without using detailed physiological information or specific parameters of the plant.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1] Adeli, H., Hung, S.L., 1995. Machine Learning−Neural Networks, Genetic Algorithms, and Fuzzy System. John Wiley, New York.

[2] Adeli, H., Park, H.S., 1998. Neurocomputing for Design Automation. CRC Press, Boca Raton, Florida.

[3] Anthoni, P.M., Unworthy, M.H., Law, B.E., Irvine, J., Baldocchi, D.D., Tuyl, S.V., Moore, D., 2002. Seasonal differences in carbon and water vapor exchange in young and old-growth ponderosa pine ecosystems. Agricultural and Forest Meteorology, 111:203-222.

[4] 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.

[5] Baldocchi, D.D., Wilson, K.B., 2001. Modeling CO2 and water vapor exchange of a temperate broadleaved forest across hourly to decadal time scales. Ecological Modelling, 142:155-184.

[6] Baldocchi, D.D., Hicks, B.B., Meyers, T.P., 1988. Measuring biosphere-atmosphere exchanges of biologically related gases with micrometeorological methods. Ecology, 69:1331-1340.

[7] Batchelor, W.D., Yang, X.B., Tschanz, A.T., 1997. Development of a neural network for soybean rust epidemics. Trans. SAE, 40:247-252.

[8] Bosveld, F.C., Bouten, W., 1992. Transpiration Dynamics of A Douglas Fir Forest. II: Parametrization of A Single Leaf Model. In: Bouten, W. (Ed.), Monitoring and Modelling Forest Hydrological Processes in Support of Acidification Research. PhD thesis, University of Amsterdam, p.163-180.

[9] Cattan, J., Mohammadi, J., 1997. Analysis of bridge condition rating using neural networks. Computer-Aided Civil and Infrastructure Engineering, 12:419-429.

[10] Chao, K., Anderson, R., 1994. Neural-fuzzy Interface System for Daily Growth of Single Stem Roses. ASAE Paper No. 94-4015, St. Joseph, MI.

[11] Cook, D.F., Wolfe, M.L., 1991. A back-propagation neural network to predict average air temperature. AI Appl, 5:40-46.

[12] Demuth, H., Beale, M., 1994. Neural Network Toolbox. For Use with MATLAB. The Math Works, Inc., Natick.

[13] Deo, M.C., Chaudhari, G., 1998. Tide prediction using neural networks. Computer-Aided Civil and Infrastructure Engineering, 13(2):113-120.

[14] Elizondo, D., Hoogenboom, G., MeClendon, R.W., 1994. Development of a neural network model to predict daily solar radiation. Agri. For. Meteorol, 71:115-132.

[15] Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G., Ceulemans, R., Cleament, R., dolman, H., 2001. Gap filling strategies for defensible annual sums of net ecosystem exchange. Agricultural and Forest Meteorology, 107:43-69.

[16] Francl, L.J., Panigrahi, S., 1997. Artificial neural network models of wheat leaf wetness. Agricultural and Forest Meteorology, 88:57-65.

[17] Goldstein, A.H., Hultman, N.E., Fracheboud, J.M., Bauer, M.R., Panek, J.A., Xu, M., Qi, Y., Guenther, A.B., Baugh, W., 2000. Effects of climate variability on the carbon dioxide, water, and sensible heat fluxes above a ponderosa pine plantation in the Sierra Nevada (CA). Agricultural and Forest Meteorology, 101(2-3):113-129.

[18] Goulden, M.L., Munger, J.W., Fan, S.M., Daube, B.C., Wofsy, S.C., 1996. Measurements of carbon storage by long-term eddy correlation: Methods and a critical evaluation of accuracy. Global Change Biology, 2:169-182.

[19] Hecht-Nielsen, R., 1987. Counterpropagation networks. Applied Optics, 26:4979-4984.

[20] Hollinger, D.Y., Kelliher, F.M., Schulze, E.D., Vgodskaya, N.N., Varlargin, A., Milukova, I., Byers, J.N., Sogachov, A., Hunt, J.E., McSeveny, T.M., Kobak, K.I., Bauer, G., Arneth, A., 1995. Initial assessment of multi-scale measures of CO2 and H2O flux in Siberian taiga. J Bio Geogr, 22:425-431.

[21] Humphreys, E.R., Black, T.A., Ethier, G.J., Drewitt, G.B., Spittlehouse, D.L., Jork, E.M., Nesic, Z., Livingston, N.J., 2003. Annual and seasonal variability of sensible and latent heat fluxes above a coastal Douglas-fir forest, British Columbia, Canada. Agricultural and Forest Meteorology, 115:109-125.

[22] Hunt, J.E., Kelliher, F.M., McSeveny, T.M., Byers, J.N., 2002. Evaporation and carbon dioxide exchange between the atmosphere and tussock grassland during a summer drought. Agricultural and Forest Meteorology, 111:65-82.

[23] 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.

[24] Jarvis, P.G., 1976. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philo. Trans. R. Soc. London, 273(B):593-610.

[25] Jarvis, P.G., 1995. Scaling processes and problems. Plant Cell Environ, 18:1079-1089.

[26] Katerji, N., Perrier, A., 1983. Modélization de l′évapotranspiration réelle ETR dune parcelle de luzerne: rôled′un coefficient cultural. Agronomie, 3(6):513-521 (in French).

[27] Kosko, B., 1992. Neural Networks and Fuzzy Systems. A Dynamical Systems Approach to Machine Intelligence. Prentice-Hall, Englewood Cliffs, New Jersey, p.449.

[28] Lee, X.H., Yu, Q., Sun, X.M., Liu, J.D., Min, Q.W., Liu, Y.F., Zhang, X.Z., 2004. Micrometeorological fluxes under the influence of regional and local advection: a revisit. Agricultural and Forest Meteorology, 122:111-124.

[29] Lek, S., Guegan, J.F., 1999. Artificial neural networks as a tool in ecological modeling and introduction. Ecol Model, 120:65-73.

[30] Lek, S., Delacoste, M., Baran, P., Dimopoulos, L., Lauga, J., Stephane, A., 1996. Application of neural networks to modeling nonlinear relationships in ecology. Ecological Modeling, 90:39-52.

[31] Leuning, R., Kelliher, F.M., Depury, D., Schulze, E.D., 1995. Leaf nitrogen, photosynthesis, conductance and transpiration:scaling from leaves to canopies. Plant Cell Environ, 18:1183-1200.

[32] Marquardt, D.W., 1963. An algorithm for least-squares estimation of non-linear parameters. SIAM, Journal on Applied mathematics, 11(2):431-441.

[33] Massman, W.J., Lee, X., 2002. Eddy covariance flux corrections and uncertainties in long-termstudies of carbon and energy exchanges. Agricultural and Forest Meteorology, 113:121-144.

[34] Moisen, G.G., Frescino, T.S., 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling, 157:209-225.

[35] Murase, H., Nishiura, Y., Honami, N., 1994. Textural Features/Neural Network for Plant Growth Monitoring. ASAE Paper No. 94-4016, St. Joseph, MI.

[36] Owusu-Ababia, S., 1998. Effect of neural network topology on flexible pavement cracking prediction. Computer-Aided Civil and Infrastructure Engineering, 13(5):349-355.

[37] Pilegaard, K., Hummelshoj, P., Jensen, N.O., Chen, Z., 2001. Two years of continuous CO2 eddy-flux measurements over a Danish beech forest. Agricultural and Forest Meteorology, 107:29-31.

[38] Sadeghi, B.H.M., 2000. A BP-neural network predictor model for plastic injection molding process. Journal of Materials Processing Technology, 103:411-416. Schelde, K., Kelliher, F.M., Massman, W.J., Jensen, K.H., 1997. Estimating sensible and latent heat fluxes from a temperate broad-leaved forest using the Simple Biosphere (SiB) model. Agricultural and Forest Meteorology, 84:285-295.

[39] Schulz, H., Härtling, S., 2003. Vitality analysis of Scots pines using a multivariate approach. Forest Ecology and Management, 186:73-84.

[40] Stewart, J.B., 1988. Modelling surface conductance of pine forest. Agric. For. Meteorol. 43:19-35.

[41] Thai, C.N., Shewfelt, R.L., 1991. Modelling sensory color quality of tomato and peach: neural networks and statistical regression. Trans. ASAE, 34:950-955.

[42] Thirumalaiah, K., Deo, M.C., 1998. Real time flow forecasting using neural networks. Computer-Aided Civil and Infrastructure Engineering, 13(2):101-111.

[43] Van Wijk, M.T., Bouten, W., 1999. Water and carbon fluxes above European coniferous forests modeled with artificial neural networks. Ecological Modelling, 120:181-197.

[44] Werner, H., Obach, M., 2001. New neural network types estimating the accuracy of response for ecological modeling. Ecological Modelling, 146:289-298.

[45] Wilson, K.B., Hanson, P.J., Baldocchi, D.D., 2000. Factors controlling evaporation and energy partitioning beneath a deciduous forest over an annual cycle. Agricultural and Forest Meteorology, 102:83-103.

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