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  Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks

Schleiter, I. M., Obach, M., Wagner, R., Werner, H., Schmidt, H.-H., & Borchardt, D. (2003). Modelling Ecological Interrelations in Running Water Ecosystems with Artificial Neural Networks. In F. Recknagel (Ed.), Ecological Informatics. Understanding Ecology by Biologically-Inspired Computation (pp. 109-126). Berlin: Springer-Verlag.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-C7EC-6 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-C7ED-4
Genre: Book Chapter

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 Creators:
Schleiter, Ingrid M., Author
Obach, Michael1, Author           
Wagner, Rüdiger1, Author           
Werner, Heinrich, Author
Schmidt, Hans-Heinrich1, Author           
Borchardt, Dietrich, Author
Affiliations:
1Limnological River Station Schlitz, Max Planck Institute for Limnology, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_976546              

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Language(s): eng - English
 Dates: 2003
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: eDoc: 63196
Other: 1029
URI: http://www.waite.adelaide.edu.au/ISEI/ecological_informatics.pdf
 Degree: -

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Title: Ecological Informatics. Understanding Ecology by Biologically-Inspired Computation
Source Genre: Book
 Creator(s):
Recknagel, Friedrich, Editor
Affiliations:
-
Publ. Info: Berlin : Springer-Verlag
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 109 - 126 Identifier: ISBN: 3540434550