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

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

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.

Suggested Citation

  • Aertsen, Wim & Kint, Vincent & van Orshoven, Jos & Özkan, Kürşad & Muys, Bart, 2010. "Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests," Ecological Modelling, Elsevier, vol. 221(8), pages 1119-1130.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:8:p:1119-1130
    DOI: 10.1016/j.ecolmodel.2010.01.007
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    8. Kint, V. & Aertsen, W. & Fyllas, N.M. & Trabucco, A. & Janssen, E. & Özkan, K. & Muys, B., 2014. "Ecological traits of Mediterranean tree species as a basis for modelling forest dynamics in the Taurus mountains, Turkey," Ecological Modelling, Elsevier, vol. 286(C), pages 53-65.
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    12. Hamid Reza Pourghasemi & Soheila Pouyan & Mojgan Bordbar & Foroogh Golkar & John J. Clague, 2023. "Flood, landslides, forest fire, and earthquake susceptibility maps using machine learning techniques and their combination," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3797-3816, April.
    13. Eslam Mohammed Abdelkader & Abobakr Al-Sakkaf & Ghasan Alfalah & Nehal Elshaboury, 2022. "Hybrid Differential Evolution-Based Regression Tree Model for Predicting Downstream Dam Hazard Potential," Sustainability, MDPI, vol. 14(5), pages 1-21, March.
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