Artificial neural networks as a tool in ecological modelling, an introduction

Artificial neural networks as a tool in ecological modelling, an introduction Artificial neural networks (ANNs) are non-linear mapping structures based on the function of the human brain. They have been shown to be universal and highly flexible function approximators for any data. These make powerful tools for models, especially when the underlying data relationships are unknown. In this reason, the international workshop on the applications of ANNs to ecological modelling was organized in Toulouse, France (December 1998). During this meeting, we discussed different methods, and their reliability to deal with ecological data. The special issue of this ecological modelling journal begins with the state-of-the-art with emphasis on the development of structural dynamic models presented by S.E. Jorgensen (DK). Then, to illustrate the ecological applications of ANNs, examples are drawn from several fields, e.g. terrestrial and aquatic ecosystems, remote sensing and evolutionary ecology. In this paper, we present some of the most important papers of the first workshop about ANNs in ecological modelling. We briefly introduce here two algorithms frequently used; (i) one supervised network, the backpropagation algorithm; and (ii) one unsupervised network, the Kohonen self-organizing mapping algorithm. The future development of ANNs is discussed in the present work. Several examples of modelling of ANNs in various areas of ecology are presented in this special issue. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecological Modelling Elsevier

Artificial neural networks as a tool in ecological modelling, an introduction

Ecological Modelling, Volume 120 (2) – Aug 17, 1999

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Publisher
Elsevier
Copyright
Copyright © 1999 Elsevier Science B.V.
ISSN
0304-3800
eISSN
1872-7026
D.O.I.
10.1016/S0304-3800(99)00092-7
Publisher site
See Article on Publisher Site

Abstract

Artificial neural networks (ANNs) are non-linear mapping structures based on the function of the human brain. They have been shown to be universal and highly flexible function approximators for any data. These make powerful tools for models, especially when the underlying data relationships are unknown. In this reason, the international workshop on the applications of ANNs to ecological modelling was organized in Toulouse, France (December 1998). During this meeting, we discussed different methods, and their reliability to deal with ecological data. The special issue of this ecological modelling journal begins with the state-of-the-art with emphasis on the development of structural dynamic models presented by S.E. Jorgensen (DK). Then, to illustrate the ecological applications of ANNs, examples are drawn from several fields, e.g. terrestrial and aquatic ecosystems, remote sensing and evolutionary ecology. In this paper, we present some of the most important papers of the first workshop about ANNs in ecological modelling. We briefly introduce here two algorithms frequently used; (i) one supervised network, the backpropagation algorithm; and (ii) one unsupervised network, the Kohonen self-organizing mapping algorithm. The future development of ANNs is discussed in the present work. Several examples of modelling of ANNs in various areas of ecology are presented in this special issue.

Journal

Ecological ModellingElsevier

Published: Aug 17, 1999

References

  • A learning algorithm for Boltzmann machines
    Ackley, D.H.; Hinton, G.E.; Sejnowski, T.J.
  • Biomass estimation in plant cell cultures: a neural network approach
    Albiol, J.; Campmajo, C.; Casas, C.; Poch, M.
  • A neural network model for survival data
    Faraggi, D.; Simon, R.
  • Modelling spatial dynamics of fish
    Giske, J.; Huse, G.; Fiksen, O.
  • Application of neural networks to modelling nonlinear relationships in ecology
    Lek, S.; Delacoste, M.; Baran, P.; Dimopoulos, I.; Lauga, J.; Aulagnier, S.
  • Artificial neural network approach for modelling and prediction of algal blooms
    Recknagel, F.; French, M.; Harkonen, P.; Yabunaka, K.I.
  • Neural networks for statistical modelling
    Smith, M.

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