Short term forecasting of algal blooms in drinking water reservoirs using artificial neural networks / Hugh Edward Campbell Wilson.
Date
2004
Authors
Wilson, Hugh Edward Campbell
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Thesis
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Abstract
Artificial neural networks (ANNs), trained to make short term forecasts of algal blooms in lakes and rivers, are potentially useful decision making tools for the operational management of eutrophication. This thesis addresses the question of whether a standardised, gemeric ANN model representation can be developed to achieve this goal. It is argued that four requirements need to be addressed: i) compatibility of models with existing water quality monitoring regimes, ii) stability and repeatability of training outcomes, iii) realistic and meaningful estimates of model performance, and iv) explanation of predictions.
School/Discipline
School of Earth and Environmental Sciences : Environmental Biology
Dissertation Note
Thesis (Ph.D.)--University of Adelaide, School of Earth and Environmental Sciences, Discipline of Environmental Biology, 2004
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This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exception. If you are the author of this thesis and do not wish it to be made publicly available or If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals
Description
"April 2004"
Bibliography: p. 285-299.
xxviii, 299p : ill., map ; 30 cm.
Bibliography: p. 285-299.
xxviii, 299p : ill., map ; 30 cm.