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Using a Parallel Distributed Processing System to Model Individual Tree Mortality

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Parallel distributed processing (PDP, also known as artificial neural network) was introduced as an alternative for modeling regular noncatastrophic individual tree mortality. A two hidden-layered PDP system was created using back-propagation as the learning procedure and the sigmoid function as the transfer function. An empirical data set was used to test the performance of the system. Using the performance of a logistic regression as a benchmark, the new system had a better fit to the data than that of the logistic regression. It was also found that, though the system was not instructed to fit the data with logistic curves, the response surface of the model closely followed a logistic response surface. This finding suggests that, based on the goodness-of-fit measure employed, the best function to model individual tree mortality may indeed be the logistic function. A PDP system can be regarded as a procedure which attacks the problems of parameter estimation and model selection simultaneously. Topics regarding the potential use of a PDP system as an alternative to modeling individual tree mortality were also discussed. For. Sci. 37(3):871-885.

Keywords: Machine learning; artificial neural network; individual tree mortality

Document Type: Journal Article

Affiliations: Associate Professor, of Forest Biometrics, Dept. of Forestry, University of Illinois, 1301 W. Gregory Drive, 110 Mumford Hall, Urbana, IL 61801

Publication date: August 1, 1991

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

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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