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Artificial Neural Network Modeling of Forest Tree Growth

Domaine:

environment and energy

Type de record:

paper
Créateur:
Gordon, Christopher
Éditeur:
arXiv
Hôte:avatar
The problem of modeling forest tree growth curves with an artificial neural network (NN) is examined. The NN parametric form is shown to be a suitable model if each forest tree plot is assumed to consist of several differently growing sub-plots. The predictive Bayesian approach is used in estimating the NN output. Data from the correlated curve trend (CCT) experiments are used. The NN predictions are compared with those of one of the best parametric solutions, the Schnute model. Analysis of variance (ANOVA) methods are used to evaluate whether any observed differences are statistically significant. From a Frequentist perspective the differences between the Schnute and NN approach are found not to be significant. However, a Bayesian ANOVA indicates that there is a 93% probability of the NN approach producing better predictions on average. 86 pages, 19 figures, submitted as a MSc research report to the University of the Witwatersrand, South Africa

Visit

doi.orgarxiv.org

Tags

Data Analysis, Statistics and Probability (physics.data-an)FOS: Physical sciencesFOS: Physical sciences

Licenses

Assumed arXiv.org perpetual, non-exclusive license to distribute this article for submissions made before January 2004http://arxiv.org/licenses/assumed-1991-2003/

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