A framework for uncertainty assessment of mechanistic forest growth models: A neural network example

Ecological Modelling

Volumn 98, Issue 1, 1997, Pages 47-58

  Guan, Biing T ^a   Gertner, George Z ^b   Parysow, Pablo ^b  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Artificial neural network; Conceptual model; Ecological modeling; Pipe model

Indexed keywords

EID: 0030967361     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3800(96)01936-9     Document Type: Article

References (25)

1. Ahrens, J.H. and Dieter, U., 1974. Computer methods for sampling from γ, β, Poisson, and binomial distributions. Computing, 12: 223-246.
2. Baba, N., 1989. A new approach for finding the global minimum of error function of neural networks. Neural Network, 2: 367-373.
3. Baffes, P.T., 1989. Nets User's Guide. Version 2.0. Software Technology Branch, Lyndon B. Johnson Space Center, Houston, Texas.
4. Bossel, H., 1991. Modeling forest dynamics: moving from description to explanation. Forest Ecology and Management, 42: 129-143.
5. Bossel, H., 1992. Real-structure process description as the basis of understanding ecosystems and their development. Ecol. Model., 63: 261-276.
6. Cybenko, G., 1989. Approximation by superposition of a sigmoidal function. Math. Controls, Signals Sys., 2: 303-314.
7. Fedra, K., Van Straten, G. and Beck, M.B., 1981. Uncertainty and arbitrariness in ecosystems modeling: a lake modelling example. Ecol. Model., 13: 87-110.
8. Gertner, G., 1987. Approximating precision in simulation projections: an efficient alternative to Monte Carlo methods. For. Sci., 33: 230-239.
9. Gertner, G., Guan, B. and Parysow, P., 1996. Partitioning of the Variance of Predictions of a Conceptual Forest Growth Model. In: Proc. Stat. Meth., Math. Comput. Sessions held at IUFRO World Congr., Finland, August, 1995. Swiss Fed. Inst. For., Snow, Landsc. Res. Birmensdorf Switzerland, pp. 11-22.
10. Geman, S., Bienenstock, E. and Dourset, R., 1991. Neural networks and the bias/variance dilemma. Neural Comput., 4(1): 1-58.
11. Guan, B.T. and Gertner, G.Z., 1997. An artificial neural network with partitionable outputs. Comput. Electron. Agric., 16(1): 39-46.
12. Guan, B.T., Gertner, G.Z. and Kowalski, D., 1993. Modeling training site vegetation coverage probability with a random optimization procedure: an artificial neural network approach. In: Proc. Conf. Appl. Artif. Neural Networks IV, Orlando, Florida, pp. 682-688.
13. Hornik, K., Stinchcombe, M. and White, H., 1989. Multilayer feedforward networks are universals approximators. Neural Network, 2: 359-366.
14. Lapedes, A. and Farber, R., 1987. Nonlinear signal processing using neural networks: prediction and system modelling. Technical Report LA-UR-87-2662, Los Alamos Natl. Lab., New Mexico.
15. Laurence, J.A., Kohut, R.J. and Amundson, R.G., 1993. Use of TREGRO to simulate the effects of ozone on the growth of red spruce seedlings. For. Sci., 39(3): 453-464.
16. Lehmann, E.L., 1975. Nonparametrics: Statistical Methods Based on Ranks. Holden-Day, San Francisco, pp. 457.
17. Parysow, P.F., 1994. A procedure to approximate prediction uncertainty in ecological conceptual models. Unpublished M.S. Thesis, University of Illinois at Urbana-Champaign, Urbana, Illinois, pp. 126.
18. Ripley, B.D., 1994. Neural networks and related methods for classification. J. R. Stat. Soc. Ser. B (Methodol.), 56 (3): 409-437.
19. Sarle, W.S., 1994. Neural networks and statistical models. In: Proc. Nineteenth Annu. SAS Users Group Int. Conf. SAS Institute, Cary, North Carolina, pp. 1538-1550.
20. Shinozaki, K., Yoda, K., Hozumi, K. and Kira, T., 1964a. A quantitative analysis of plant form - the pipe model theory. I. Basic analysis. Jpn. J. For. Ecol., 14: 97-105.
21. Shinozaki, K., Yoda, K., Hozumi, K. and Kira, T., 1964b. A quantitative analysis of plant form - the pipe model theory. II. Further evidence of the theory and its application in forest ecology. Jpn. J. For. Ecol., 14: 133-139.
22. Szu, H. and Hartley, R., 1987. Fast simulated annealing. Phys. Lett. (Ser. A), 122: 157-162.
23. Valentine, H., 1988. A carbon balance model of stand growth: a derivation employing pipe-model theory and the self-thinning rule. Ann. Bot., 62: 389-396.
24. White, H., 1989a. Learning in artificial neural networks: a statistical perspective. Neural Comput., 1: 425-464.
25. White, H., 1989b. Some asymptotic results of learning in single hidden-layer feedforward network models. J. Am. Stat. Assoc., 84: 1003-1013.