Modelling algal growth and transport in rivers: a comparison of time series analysis, dynamic mass balance and neural network techniques

Modelling algal growth and transport in rivers: a comparison of time series analysis, dynamic... Algae present considerable problems for river qualitymanagers and water suppliers and methods to predicttheir behaviour, growth and transport can assist inoperational management. Alternative techniques existfor predicting algal response and three approacheshave been compared and applied to data from six sitesalong the River Thames. These techniques include timeseries analysis, dynamic mass balance and growthequations and neural network approaches. It is shownthat neural network techniques offer a new approachrequiring less intuitive knowledge but predictivecapability is not improved greatly compared to otherapproaches. Neural networks enable models to bedeveloped along all six reaches of the RiverThames. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Hydrobiologia Springer Journals

Modelling algal growth and transport in rivers: a comparison of time series analysis, dynamic mass balance and neural network techniques

Hydrobiologia, Volume 349 (3) – Sep 2, 2004

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Publisher
Springer Journals
Copyright
Copyright © 1997 by Kluwer Academic Publishers
Subject
Life Sciences; Freshwater & Marine Ecology; Ecology; Zoology
ISSN
0018-8158
eISSN
1573-5117
DOI
10.1023/A:1003089310834
Publisher site
See Article on Publisher Site

Abstract

Algae present considerable problems for river qualitymanagers and water suppliers and methods to predicttheir behaviour, growth and transport can assist inoperational management. Alternative techniques existfor predicting algal response and three approacheshave been compared and applied to data from six sitesalong the River Thames. These techniques include timeseries analysis, dynamic mass balance and growthequations and neural network approaches. It is shownthat neural network techniques offer a new approachrequiring less intuitive knowledge but predictivecapability is not improved greatly compared to otherapproaches. Neural networks enable models to bedeveloped along all six reaches of the RiverThames.

Journal

HydrobiologiaSpringer Journals

Published: Sep 2, 2004

References

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