Measuring treatment effectiveness of urban wetland using hybrid water quality - Artificial neural network (ANN) model

Singh, G; Kandasamy, J; Shon, HK; Chob, J

Measuring treatment effectiveness of urban wetland using hybrid water quality - Artificial neural network (ANN) model

Singh, G Kandasamy, J Shon, HK

Chob, J

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Publication Type:: Journal Article
Citation:: Desalination and Water Treatment, 2011, 32 (1-3), pp. 284 - 290
Issue Date:: 2011-01-01

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Field	Value	Language
dc.contributor.author	Singh, G	en_US
dc.contributor.author	Kandasamy, J	en_US
dc.contributor.author	Shon, HK https://orcid.org/0000-0002-3777-7169	en_US
dc.contributor.author	Chob, J	en_US
dc.date.issued	2011-01-01	en_US
dc.identifier.citation	Desalination and Water Treatment, 2011, 32 (1-3), pp. 284 - 290	en_US
dc.identifier.issn	1944-3994	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31228
dc.description.abstract	Constructed wetlands are now commonly used as tertiary treatment for urban stormwater. The wetlands have primary advantage over other forms of treatment as they remove dissolved organics and heavy metals in conjunction with other pollutants. The effectiveness of a wetland is a primary concern for validating its compliance with design objectives and regulatory requirements. The treatment in a wetland is however complex and is dependent on input pollutants, hydraulics, physico-chemical balance and biota within the wetland. Several models are available for wetlands but have limitations in simulating the physico-chemical and biological processes within the wetland. The aim of this paper is to introduce a hybrid modelling approach that involves both a deterministic model and artificial neural network (ANN) for testing the effectiveness of a constructed wetland at Olympic Park, Homebush, Sydney, Australia. This novel approach allows a combination of calibrated water quality and neural based models to predict the water quality from the wetland. The models were calibrated and validated using water quality monitoring data measured for eight months in both influent and effluent streams of the wetland. The calibrated hybrid models were then tested for treatment effectiveness for range of wet, dry and median flows conditions within the catchments. A water quality index was developed and used to quantify the effectiveness of the wetland. © 2011 Desalination Publications. All rights reserved.	en_US
dc.relation.ispartof	Desalination and Water Treatment	en_US
dc.relation.isbasedon	10.5004/dwt.2011.2712	en_US
dc.title	Measuring treatment effectiveness of urban wetland using hybrid water quality - Artificial neural network (ANN) model	en_US
dc.type	Journal Article
utslib.citation.volume	1-3	en_US
utslib.citation.volume	32	en_US
utslib.for	0904 Chemical Engineering	en_US
utslib.for	0905 Civil Engineering	en_US
utslib.for	0907 Environmental Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	closed_access
pubs.issue	1-3	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

Constructed wetlands are now commonly used as tertiary treatment for urban stormwater. The wetlands have primary advantage over other forms of treatment as they remove dissolved organics and heavy metals in conjunction with other pollutants. The effectiveness of a wetland is a primary concern for validating its compliance with design objectives and regulatory requirements. The treatment in a wetland is however complex and is dependent on input pollutants, hydraulics, physico-chemical balance and biota within the wetland. Several models are available for wetlands but have limitations in simulating the physico-chemical and biological processes within the wetland. The aim of this paper is to introduce a hybrid modelling approach that involves both a deterministic model and artificial neural network (ANN) for testing the effectiveness of a constructed wetland at Olympic Park, Homebush, Sydney, Australia. This novel approach allows a combination of calibrated water quality and neural based models to predict the water quality from the wetland. The models were calibrated and validated using water quality monitoring data measured for eight months in both influent and effluent streams of the wetland. The calibrated hybrid models were then tested for treatment effectiveness for range of wet, dry and median flows conditions within the catchments. A water quality index was developed and used to quantify the effectiveness of the wetland. © 2011 Desalination Publications. All rights reserved.

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http://hdl.handle.net/10453/31228