Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests

Wim Aertsen, Vincent Kint, Jos van Orshoven, Kürşad Özkan and Bart Muys

Ecological Modelling, 2010, vol. 221, issue 8, 1119-1130

Abstract: Forestry science has a long tradition of studying the relationship between stand productivity and abiotic and biotic site characteristics, such as climate, topography, soil and vegetation. Many of the early site quality modelling studies related site index to environmental variables using basic statistical methods such as linear regression. Because most ecological variables show a typical non-linear course and a non-constant variance distribution, a large fraction of the variation remained unexplained by these linear models. More recently, the development of more advanced non-parametric and machine learning methods provided opportunities to overcome these limitations. Nevertheless, these methods also have drawbacks. Due to their increasing complexity they are not only more difficult to implement and interpret, but also more vulnerable to overfitting. Especially in a context of regionalisation, this may prove to be problematic. Although many non-parametric and machine learning methods are increasingly used in applications related to forest site quality assessment, their predictive performance has only been assessed for a limited number of methods and ecosystems.

Keywords: Artificial neural networks; Boosted regression trees; Forest site classification; Generalized additive models; Multi-criteria decision analysis; Multiple linear regression; Predictive modelling (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:221:y:2010:i:8:p:1119-1130

DOI: 10.1016/j.ecolmodel.2010.01.007

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