Skip to Main Content
U.S. Forest Service
Caring for the land and serving people

United States Department of Agriculture

Home > Search > Publication Information

  1. Share via EmailShare on FacebookShare on LinkedInShare on Twitter
    Dislike this pubLike this pub
    Author(s): Daniel J. Leduc; Thomas G. Matney; Keith L. Belli; V. Clark Baldwin
    Date: 2001
    Source: Res. Pap. SRS-25.Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 24p.
    Publication Series: Research Paper (RP)
    Station: Southern Research Station
    PDF: Download Publication  (1.5 MB)


    Artificial neural networks (NN) are becoming a popular estimation tool. Because they require no assumptions about the form of a fitting function, they can free the modeler from reliance on parametric approximating functions that may or may not satisfactorily fit the observed data. To date there have been few applications in forestry science, but as better NN software and fitting algorithms become available, they may be used to solve a wide variety of problems-particularly problems where the underlying relationship between predicted and predictors is unknown. We benchmark tested an aitemative to the traditional Weibull probability distribution function, diameter-at-breast-height moment, and direct parameter prediction models for approximating stand-diameter distributions. Using a feedforward backpropagation network, we demonstrated that NN are a somewhat better option. Unlike Weibull approximations, NN solutions cannot easily be mathematically constrained to match known reality constraints, but this difficulty is easy to overcome in practice.

    Publication Notes

    • You may send email to to request a hard copy of this publication.
    • (Please specify exactly which publication you are requesting and your mailing address.)
    • We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
    • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.


    Leduc, Daniel J.; Matney, Thomas G.; Belli, Keith L.; Baldwin, V. Clark, Jr. 2001. Predicting Diameter Distributions of Longleaf Pine Plantations: A Comparison Between Artificial Neural Networks and Other Accepted Methodologies. Res. Pap. SRS-25.Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station. 24p.


    Connectionist models, parallel distributed processing systems, parameter recovery, Weibull distribution

    Related Search

    XML: View XML
Show More
Show Fewer
Jump to Top of Page