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  5. Prediction of Forestry Planned End Products Using Dirichlet Regression and Neural Networks
 
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Prediction of Forestry Planned End Products Using Dirichlet Regression and Neural Networks

File(s)
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Download Coillte_DR_NN_2014_Version_August.pdf1.26 MB
Author(s)
Hickey, Ciarán 
Kelly, Stephen 
Carroll, Paula 
O'Connor, John J. 
Uri
http://hdl.handle.net/10197/8700
Date Issued
12 April 2015
Date Available
28T11:28:45Z July 2017
Abstract
We describe a set of nonparametric and machine learning models to forecast the proportion of planned end products (PEP) that can be extracted from a forest compartment. We determine which forest crop attributes are significant in predicting the product proportions (of sawlog, pallet, stake, and pulp) based on an Irish data set supplied by Coillte, the Irish state forestry company. Dirichlet regression and neural networks are applied to predict the product proportions and evaluated against a multivariate multiple regression benchmark model. Based on predictive performance, the neural network performs slightly better in comparison to Dirichlet regression. However, assessing the model logic and taking account of user interpretation, the Dirichlet regression outperforms the neural network. Both models are also compared to an existing rule-based model used by Coillte. The nonparametric and machine learning techniques provided consistent reliable models to accurately predict the PEP proportions. The two proposed models extend the versatility of nonparametric and machine learning techniques to areas such as forestry.
Type of Material
Journal Article
Publisher
Society of American Foresters
Journal
Forest Science
Volume
61
Issue
2
Start Page
289
End Page
297
Keywords
  • Forestry product fore...

  • Dirichlet regression

  • Artificial neural net...

DOI
10.5849/forsci.14-023
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Owning collection
Business Research Collection
Scopus© citations
7
Acquisition Date
Jan 25, 2023
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1221
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Jan 26, 2023
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Downloads
281
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