Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/22964
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Type: Journal article
Title: Forecasting chlorine residuals in a water distribution system using a general regression neural network
Author: Bowden, G.
Nixon, J.
Dandy, G.
Maier, H.
Holmes, M.
Citation: Mathematical and Computer Modelling, 2006; 44(5-6):469-484
Publisher: Pergamon-Elsevier Science Ltd
Issue Date: 2006
ISSN: 0895-7177
Statement of
Responsibility: 
Gavin J. Bowden, John B. Nixon, Graeme C. Dandy, Holger R. Maier and Mike Holmes
Abstract: In a water distribution system (WDS), chlorine disinfection is important in preventing the spread of waterborne diseases. The ability to forecast chlorine residuals at strategic points in the WDS would be a significant aid to water quality managers in helping them to ensure the satisfaction and safety of their customers. In this research, general regression neural networks (GRNNs) are developed for forecasting chlorine residuals in the Myponga WDS, to the south of Adelaide, South Australia, up to 72 h in advance. A number of critical model issues are addressed including: the selection of an appropriate forecasting horizon; the division of the available data into subsets for modelling; and the determination of the inputs relevant to the chlorine forecasts. To determine if the GRNN is able to capture any nonlinear relationships that may be present in the data set, a comparison is made between the GRNN model and a multiple linear regression (MLR) model. Additional investigations are also performed to simulate the effects of a reduced sampling frequency, and to estimate model performance for longer lead-time forecasts. When tested on an independent validation set of data, the GRNN models are able to forecast chlorine levels to a high level of accuracy, up to 72 h in advance. The GRNN also significantly outperforms the MLR model, thereby providing evidence of the existence of nonlinear relationships in the data set.
Keywords: Artificial neural networks; Forecasting; Chlorine residual; Water distribution system; General regression neural network
Description: Copyright © 2007 Elsevier B.V
RMID: 0020061135
DOI: 10.1016/j.mcm.2006.01.006
Description (link): http://www.elsevier.com/wps/find/journaldescription.cws_home/623/description#description
Appears in Collections:Civil and Environmental Engineering publications
Environment Institute publications

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