Performance modelling in forest operations through partial least square regression

C Costa; P Menesatti; Raffaele Spinelli

doi:10.14214/sf.57

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Performance modelling in forest operations through partial least square regression

Journal article

Open access

Peer reviewed

Performance modelling in forest operations through partial least square regression

C Costa, P Menesatti and Raffaele Spinelli

Silva Fennica, Vol.46(2), pp.241-252

2012

DOI: https://doi.org/10.14214/sf.57

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Abstract

chipping

harvesting

productivity

skidding

Partial Least Square (PLS) regression is a recent soft-modelling technique that generalizes and combines features from principal component analysis (PCA) and multiple regression. It is particularly useful when predicting one or more dependent variables from a large set of independent variables, often collinear. The authors compared the potential of PLS regression and ordinary linear regression for accurate modelling of forest work, with special reference to wood chipping, wood extraction and the continuous harvesting of short rotation coppice. Compared to linear regression, PLS regression allowed producing models that better fit the original data. What is more, it allowed handling collinear variables, facilitating the extraction of sound models from large amounts of field data obtained from commercial forest operations. On the other hand, PLS regression analysis is not as easy to conduct, and produces models that are less user-friendly. By producing alternative models, PLS regression may provide additional - and not alternative - ways of reading the data. Ideally, a comprehensive data analysis could include both ordinary and PLS regression and proceed from their results in order to get a better understanding of the phenomenon under examination. Furthermore, the computational complexity of PLS regression may stimulate interdisciplinary team-building, to the greater benefit of scientific research within the field of forest operations.

Details

Title: Performance modelling in forest operations through partial least square regression
Authors: C Costa (Author) - CRA-ING, Italy
P Menesatti (Author) - CRA-ING, Italy
Raffaele Spinelli (Author) - CNR IVALSA, Italy
Publication details: Silva Fennica, Vol.46(2), pp.241-252
Publisher: Suomen Metsatieteellinen Seura
Date published: 2012
DOI: 10.14214/sf.57
ISSN: 0037-5330
Copyright note: Copyright © 2012 Finnish Society of Forest Science. Reproduced here in accordance with the publishers copyright policy.
Organisation Unit: University of the Sunshine Coast, Queensland; Forest Industries Research Centre; Forest Research Institute
Language: English
Record Identifier: 99448808402621
Output Type: Journal article

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Collaboration types: Domestic collaboration
Web Of Science research areas: Forestry

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