Please use this identifier to cite or link to this item:
Type: Thesis
Title: Forecasting water resources variables using artificial neural networks / by Gavin James Bowden.
Author: Bowden, G. J. (Gavin James)
Issue Date: 2003
School/Discipline: School of Civil and Environmental Engineering
Abstract: A methodology is formulated for the successful design and implementation of artificial neural networks (ANN) models for water resources applications. Attention is paid to each of the steps that should be followed in order to develop an optimal ANN model; including when ANNs should be used in preference to more conventional statistical models; dividing the available data into subsets for modelling purposes; deciding on a suitable data transformation; determination of significant model inputs; choice of network type and architecture; selection of an appropriate performance measure; training (optimisation) of the networks weights; and, deployment of the optimised ANN model in an operational environment. The developed methodology is successfully applied to two water resorces case studies; the forecasting of salinity in the River Murray at Murray Bridge, South Australia; and the the forecasting of cyanobacteria (Anabaena spp.) in the River Murray at Morgan, South Australia.
Dissertation Note: Thesis (Ph.D.)--University of Adelaide, School of Civil and Environmental Engineering, 2003
Subject: Saline waters South Australia Murray River Mathematical models.
Cyanobacteria South Australia Murray River Mathematical models.
Neural networks (Computer science)
Civil engineering Data processing.
Computer-aided engineering.
Description: "February 2003."
Corrigenda for, inserted at back
Includes bibliographical references (leaves 475-524 )
xxx, 524 leaves : ill. ; 30 cm.
Provenance: 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:
Appears in Collections:Research Theses

Files in This Item:
File Description SizeFormat 
01front.pdf311.25 kBAdobe PDFView/Open
02whole.pdf11.25 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.