Modelling Ecological Interrelations in Running Water Ecosystems with Artificial 
Neural Networks

Schleiter, Ingrid M.; Obach, Michael; Wagner, Rüdiger; Werner, Heinrich; Schmidt, Hans-Heinrich; Borchardt, Dietrich

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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

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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: Date issued: 2003

Publication Status: Issued

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Rev. Type: Peer

Identifiers: eDoc: 63196
Other: 1029
URI: http://www.waite.adelaide.edu.au/ISEI/ecological_informatics.pdf

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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