Artificial Neural Network Modeling of Dissolved Oxygen in a Wetland Pond: The Case of Hovi, Finland
Publication: Journal of Hydrologic Engineering
Volume 11, Issue 2
Abstract
Artificial neural networks (ANNs) are flexible tools from neuroinformatics that have performed well in a number of hydrologic applications so far. They tend to be particularly useful when applied to complex processes, the details of which are not well understood. The dissolved oxygen regime in constructed wetland ponds is, in turn, such a complex process, governed by a considerable number of hydrologic, hydrodynamic, and ecological controls which operate at a wide range of spatiotemporal scales. This paper reports on the results from a study conducted to test the adequacy of artificial neural networks in modeling near-bottom concentrations of dissolved oxygen in the Finnish free water surface wetland at Hovi. Various different networks of the multilayer perceptron (MLP) type of ANN were developed. The application proved successful, and in particular it was observed that MLPs were able to “learn” the mechanism of convective oxygen transport quite well. The ANN was also used to determine the relative influence of flow rate and wind shear on near bottom oxygen saturation.
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Acknowledgement
The work reported here was part of a research effort funded by the European Commission under Contract No. UNSPECIFIEDPRIMROSE EVK1-2000-00065.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 11 • Issue 2 • March 2006
Pages: 188 - 192
Copyright
© 2006 ASCE.
History
Received: Aug 31, 2004
Accepted: Mar 21, 2005
Published online: Mar 1, 2006
Published in print: Mar 2006
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