Journal
ECOLOGICAL MODELLING
Volume 146, Issue 1-3, Pages 303-310Publisher
ELSEVIER
DOI: 10.1016/S0304-3800(01)00316-7
Keywords
machine learning; ecological modelling; artificial neural network; genetic algorithm; multivariate statistics; time-series modelling; model hybridization; knowledge discovery; adaptive agents
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The paper provides a summary of paper presentations at the 2nd International Conference on Applications of Machine Learning to Ecological Modelling and a preview of forthcoming developments in this area. Artificial neural networks were demonstrated to be very useful for nonlinear ordination and visualization of ecological data by Kohonen networks, and ecological time-series modelling by recurrent networks. Genetic algorithms proved to be very innovative for hybridizing deductive models, and evolving predictive rules, process equations and parameters. Newly emerging adaptive agents provide a novel framework for the discovery and forecasting of emergent ecosystem structures and behaviours in response to environmental changes. (C) 2001 Elsevier Science B.V. All rights reserved.
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