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Effects of Correlation among Parameters on Prediction Quality of a Process-Based Forest Growth Model

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A nonparametric method was introduced as a technique for evaluating the effects of parameter correlation on prediction quality of process-based forest growth models. The method was based on a rank correlation and Cholesky decomposition. For a given data matrix, by reordering the observations, the method would produce a rearranged matrix with the desired correlation structure. The method was computationally simple and efficient. Using a process-based model calibrated for red pine (Pinus resinosa Ait.) as an example, small-scale Monte Carlo simulations revealed that parameter correlation had different effects on the model's prediction means and variances, depending on the importance of the parameters involved. In general, given the same level of correlation, correlation between two important parameters would have greater influences on prediction quality than correlation between two less important parameters. Parameter correlation slightly affected the prediction means but could significantly change prediction variances. The relationship between prediction quality and the degrees of correlation, however, was not necessarily a linear one. The same parameter correlation might also have different effects on different state variables. For. Sci. 46(2):269-276.

Keywords: Cholesky decomposition; Ecological model; rank correlation; uncertainty analysis

Document Type: Journal Article

Publication date: 2000-05-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 in forestry

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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