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Predicting Douglas-fir wood density by artificial neural networks (ANN) based on progeny testing information

  • Lazaros Iliadis , Shawn D. Mansfield , Stavros Avramidis and Yousry A. El-Kassaby EMAIL logo
Published/Copyright: March 18, 2013
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Abstract

A heuristic wood density prediction model has been developed by means of artificial neural networks (ANNs). Four populations of 32-year-old coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco var. menziesi) trees representing 20 full-sib families growing on comparable sites were in focus of this study. Tree height, diameter, volume, wood density, and acoustic velocity data from 632 trees were considered for the calculations. Two different ANN platforms were developed employing different classes and architectures, namely, the multilayer feed-forward (MLFF) and modular (MOD) models. After establishing the optimal configuration of the model, a MLFF network and a MOD neural network (with the obtained optimal structure) were developed and tested without cross-validation by employing a typical training and testing set methodology. To this purpose, the data set was divided in 480 trees for training and 152 trees for validation. A significant relationship between actual and predicted wood density was obtained with R2 values of 0.50 and 0.52 for the two networks, respectively, demonstrating their predictive potential for wood density estimation. A classic multiple regression analysis produced substantially lower predictive power with an R2 of 0.23. The application of ANNs as a viable predictive tool in determining wood density using growth and acoustic velocity data without additional intrusive sampling and laboratory work was demonstrated. An additional work including other species is required for these approaches.


Corresponding author: Yousry A. El-Kassaby, Faculty of Forestry, Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, V6T 1Z4 Canada, Phone: +1 (604) 822-1821, e-mail:

We thank M. Stoehr for providing access to the research material. Funds from the Natural Sciences and Engineering Research Council of Canada-Discovery and IRC grants to Y.A.K. are highly appreciated.

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Received: 2012-8-1
Accepted: 2013-2-5
Published Online: 2013-03-18
Published in Print: 2013-10-01

©2013 by Walter de Gruyter Berlin Boston

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