Application of neural networks to modelling nonlinear relationships in ecology

Application of neural networks to modelling nonlinear relationships in ecology Two predictive modelling principles are discussed: multiple regression (MR) and neural networks (NN). The MR principle of linear modelling often gives low performance when relationships between variables are nonlinear; this is often the case in ecology; some variables must therefore be transformed. Despite these manipulations, the results often remain disappointing: poor prediction, dependence of residuals on the variable to predict. On the other hand NN are nonlinear type models. They do not necessitate transformation of variables and can give better results. The application of these two techniques to a set of ecological data (study of the relationship between density of brown trout spawning sites (redds) and habitat characteristics), shows that NN are clearly more performant than MR ( R 2 = 0.96 vs R 2 = 0.47 or R 2 = 0.72 in raw variables or nonlinear transformed variables). With the calculation power now currently available, NN are easy to implement and can thus be recommended for modelling of a number ecological processes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Application of neural networks to modelling nonlinear relationships in ecology

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Publisher
Elsevier
Copyright
Copyright © 1996 Elsevier Ltd
ISSN
0304-3800
eISSN
1872-7026
DOI
10.1016/0304-3800(95)00142-5
Publisher site
See Article on Publisher Site

Abstract

Two predictive modelling principles are discussed: multiple regression (MR) and neural networks (NN). The MR principle of linear modelling often gives low performance when relationships between variables are nonlinear; this is often the case in ecology; some variables must therefore be transformed. Despite these manipulations, the results often remain disappointing: poor prediction, dependence of residuals on the variable to predict. On the other hand NN are nonlinear type models. They do not necessitate transformation of variables and can give better results. The application of these two techniques to a set of ecological data (study of the relationship between density of brown trout spawning sites (redds) and habitat characteristics), shows that NN are clearly more performant than MR ( R 2 = 0.96 vs R 2 = 0.47 or R 2 = 0.72 in raw variables or nonlinear transformed variables). With the calculation power now currently available, NN are easy to implement and can thus be recommended for modelling of a number ecological processes.

Journal

Ecological ModellingElsevier

Published: Sep 1, 1996

References

  • Habitat evaluation as a fisheries management tool
    Milner, N.J.; Hemsworth, R.J.; Jones, B.E.
  • Observation on the structure of brown trout, Salmo trutta Linnaeus, redds
    Ottaway, E.M.; Carling, P.A.; Clarke, A.; Reader, N.A.
  • Neural Networks for Statistical Modelling
    Smith, M.
  • Phoneme recognition using time-delay neural networks
    Waibel, A.; Hanazawa, T.; Hinton, G.; Shikano, K.; Lang, K.J.
  • Quantitative analysis of qualitative data
    Young, F.W.

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