Simulating CO2 flux of three different ecosystems in ChinaFLUX based on artificial neural networks

Science in China, Series D: Earth Sciences

Volumn 49, Issue SUPPL. 2, 2006, Pages 252-261

  He, Honglin ^a   Yu, Guirui ^a   Zhang, Leiming ^a   Sun, Xiaomin ^a   Su, Wen ^a  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

Author keywords

Artificial neural network; ChinaFLUX; CO2; Energy flux; Variables contribution

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BLACK-BOX MODEL; CARBON FLUXES; CONTROLLING PARAMETERS; EDDY COVARIANCE TECHNIQUE; ENERGY FLUXES; ERROR BACK PROPAGATION ALGORITHM; FLUX DYNAMICS; FLUX SIMULATION; GLOBAL CO; HIGHLY NONLINEAR; INPUT AND OUTPUTS; INPUT VARIABLES; MULTI-LAYER PERCEPTION; NET RADIATION; NON-LINEARITY; POINT MEASUREMENT; SENSIBLE HEAT; SOIL HEAT FLUX; WATER STRESS;

CARBON DIOXIDE; ECOSYSTEMS; GROUNDWATER; MOISTURE DETERMINATION; PERMITTIVITY; SOIL MOISTURE;

NEURAL NETWORKS;

EID: 76649124883     PISSN: 10069313     EISSN: 18622801     Source Type: Journal    
DOI: 10.1007/s11430-006-8252-z     Document Type: Article

References (18)

1. Yu G R, Zhang L M, Sun X M. Advance in carbon flux observation and research in Asia. Sci China Ser D-Earth Sci, 2005, 48(Supp I): 1-16.
2. Lek S, Guegan J F. Artificial neural networks as a tool in ecological modeling and introduction. Ecol Odel, 1999, 120: 65-73.
3. Lek S, Delacoste M, Baran P, et al. Application of neural networks to modeling nonlinear relationships in ecology. Ecological Modeling, 1996, 90: 39-52.
4. Francl L J, Panigrahi S. Artificial neural network models of wheat leaf wetness. Agricultural and Forest Meteorology, 1997, 88: 57-65.
5. Elizondo D, Hoogenboom G, MeClendon R W. Development of a neural network model to predict daily solar radiation. Agri For Meteorol, 1994, 71: 115-132.
6. van Wijk M T, Bouten W. Water and carbon fluxes above European coniferous forest modeled with artificial neural networks. Ecological Modeling, 1999, 120: 181-197.
7. Papale D, Valentini R. A new assessment of European forests carbon exchanges by eddy fluxes and artificial neural network spatialization. Global Change Biology, 2003, 9(4): 525-535.
8. Assefa M M, Rondney S H. Artificial neural network application for multi-ecosystem carbon flux simulation. Ecological Modeling, 2005, 189: 305-314.
9. van Wijk M T, Bouten, Verstraten J M. Comparison of different modeling strategies for simulating gas exchange of a Douglas-fir forest. Ecological Modeling, 2002, 158: 63-81.
10. Wang K C, Zhou X J. Using satellite remotely sensed data to retrieve sensible and latent heat fluxes: a review. Advance in Earth Sciences, 2005, 20(1): 42-48.
11. Aubinet M, Grelle A, Ibrom A, et al. Estimates of the annual net carbon and water exchange of forests: the EUROFLUX methodology. Advance in Ecological Research, 2000, (30): 113-175.
12. Olson R J, Hollady S K, Cook R B, et al. Fluxnet: Database of Fluxes. Site Characteristics, and Flux-Community Information. Oak Ridge National Laboratory. ORNL/TM-2003/204.
13. 2 flux above broad-leaved Korean pine mixed forest in Changbai Mountains. Sci China Ser D-Earth Sci, 2005, 48(Supp I): 123-132.
14. Qing Z, Yu Q, Xu S H. Water, heat fluxes and water use efficiency measurement and modeling above a farmland in the North China Plain. Sci China Ser D-Earth Sci, 2005, 48(Supp. I): 207-217.
15. 2 fluxes for growing and non-growing seasons in a shrub ecosystem on the Qinghai-Tibet Plateau. Sci China Ser D-Earth Sci, 2005, 48(Supp I): 133-140.
16. Gevrey M, Diomopoulos I, Lek S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modeling, 2003, (160): 249-264.
17. Julian D O, Michael K J, Russell G D. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modeling, 2004, (178): 389-397.
18. Valentini R, ed. Fluxes of Carbon, Water, Energy of European Forests: Ecological Studies. Vol. 163. Berlin: Springer, 2003.