Performance modelling in forest operations through partial least square regression

Abstract

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.