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Wood Research and Technology

Holzforschung

Cellulose – Hemicelluloses – Lignin – Wood Extractives

Editor-in-Chief: Salmén, Lennart

Editorial Board: Daniel, Geoffrey / Militz, Holger / Rosenau, Thomas / Sixta, Herbert / Vuorinen, Tapani / Argyropoulos, Dimitris S. / Balakshin, Yu / Barnett, J. R. / Burgert, Ingo / Rio, Jose C. / Evans, Robert / Evtuguin, Dmitry V. / Frazier, Charles E. / Fukushima, Kazuhiko / Gindl-Altmutter, Wolfgang / Glasser, W. G. / Holmbom, Bjarne / Isogai, Akira / Kadla, John F. / Koch, Gerald / Lachenal, Dominique / Laine, Christiane / Mansfield, Shawn D. / Morrell, J.J. / Niemz, Peter / Potthast, Antje / Ragauskas, Arthur J. / Ralph, John / Rice, Robert W. / Salin, Jarl-Gunnar / Schmitt, Uwe / Schultz, Tor P. / Sipilä, Jussi / Takano, Toshiyuki / Tamminen, Tarja / Theliander, Hans / Welling, Johannes / Willför, Stefan / Yoshihara, Hiroshi


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Volume 67, Issue 7

Issues

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

Lazaros Iliadis
  • Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, 68200 Nea Orestias, Greece
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/ Shawn D. Mansfield
  • Faculty of Forestry, Department of Wood Science, University of British Columbia, Vancouver, British Columbia, V6T 1Z4 Canada
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/ Stavros Avramidis
  • Faculty of Forestry, Department of Wood Science, University of British Columbia, Vancouver, British Columbia, V6T 1Z4 Canada
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/ Yousry A. El-Kassaby
  • Corresponding author
  • Faculty of Forestry, Department of Forest and Conservation Sciences, University of British Columbia, Vancouver, British Columbia, V6T 1Z4 Canada
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Published Online: 2013-03-18 | DOI: https://doi.org/10.1515/hf-2012-0132

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.

Keywords: artificial neutral networks (ANNs); douglas-fir; modular (MOD) model; wood density; multilayer feed-forward method (MLFF)

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About the article

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:


Received: 2012-08-01

Accepted: 2013-02-05

Published Online: 2013-03-18

Published in Print: 2013-10-01


Citation Information: Holzforschung, Volume 67, Issue 7, Pages 771–777, ISSN (Online) 1437-434X, ISSN (Print) 0018-3830, DOI: https://doi.org/10.1515/hf-2012-0132.

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