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https://hdl.handle.net/2440/27266
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DC Field | Value | Language |
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dc.contributor.author | Recknagel, F. | - |
dc.date.issued | 2001 | - |
dc.identifier.citation | Ecological Modelling, 2001; 146(1-3):303-310 | - |
dc.identifier.issn | 0304-3800 | - |
dc.identifier.issn | 1872-7026 | - |
dc.identifier.uri | http://hdl.handle.net/2440/27266 | - |
dc.description.abstract | 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. © 2001 Elsevier Science B.V. All rights reserved. | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Science BV | - |
dc.source.uri | http://dx.doi.org/10.1016/s0304-3800(01)00316-7 | - |
dc.title | Applications of machine learning to ecological modelling | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/S0304-3800(01)00316-7 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Recknagel, F. [0000-0002-1028-9413] | - |
Appears in Collections: | Aurora harvest 6 Environment Institute publications Soil and Land Systems publications |
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