Proactive management of estuarine algal blooms using an automated monitoring buoy coupled with an artificial neural network

Coad, P; Cathers, B; Ball, JE; Kadluczka, R

Proactive management of estuarine algal blooms using an automated monitoring buoy coupled with an artificial neural network

Coad, P Cathers, B Ball, JE

Kadluczka, R

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Publication Type:: Journal Article
Citation:: Environmental Modelling and Software, 2014, 61 pp. 393 - 409
Issue Date:: 2014-11-01

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Field	Value	Language
dc.contributor.author	Coad, P	en_US
dc.contributor.author	Cathers, B	en_US
dc.contributor.author	Ball, JE https://orcid.org/0000-0001-8342-1916	en_US
dc.contributor.author	Kadluczka, R	en_US
dc.date.issued	2014-11-01	en_US
dc.identifier.citation	Environmental Modelling and Software, 2014, 61 pp. 393 - 409	en_US
dc.identifier.issn	1364-8152	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35628
dc.description.abstract	© 2014. Algae proliferate when favourable biological, chemical and physical conditions are present. Algal blooms within the Hawkesbury River, NSW, are a regular feature of seasonal cycles and develop in response to non-periodic disturbances. To improve the understanding of processes that lead to algal blooms, an autonomous buoy has been deployed (since 2002) which has generated a high resolution, temporal data set. Parameters monitored at 15min intervals include Chlorophyll-a, temperature (water and air), salinity and photosynthetically available radiation. This data set is used to configure an Artificial Neural Network (ANN) to predict (one, three and seven days in advance) the mean, 10th and 90th percentile, daily Chlorophyll-a concentrations. The prediction accuracy of the ANNs progressively decreased from one to seven days in advance. Incorporating predictive models coupled with near real time data sourced from automated, telemetered monitoring buoys enables environmental managers to implement proactive algal bloom management strategies.	en_US
dc.relation.ispartof	Environmental Modelling and Software	en_US
dc.relation.isbasedon	10.1016/j.envsoft.2014.07.011	en_US
dc.subject.classification	Environmental Engineering	en_US
dc.title	Proactive management of estuarine algal blooms using an automated monitoring buoy coupled with an artificial neural network	en_US
dc.type	Journal Article
utslib.citation.volume	61	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.publication-status	Published	en_US
pubs.volume	61	en_US

Abstract:

© 2014. Algae proliferate when favourable biological, chemical and physical conditions are present. Algal blooms within the Hawkesbury River, NSW, are a regular feature of seasonal cycles and develop in response to non-periodic disturbances. To improve the understanding of processes that lead to algal blooms, an autonomous buoy has been deployed (since 2002) which has generated a high resolution, temporal data set. Parameters monitored at 15min intervals include Chlorophyll-a, temperature (water and air), salinity and photosynthetically available radiation. This data set is used to configure an Artificial Neural Network (ANN) to predict (one, three and seven days in advance) the mean, 10th and 90th percentile, daily Chlorophyll-a concentrations. The prediction accuracy of the ANNs progressively decreased from one to seven days in advance. Incorporating predictive models coupled with near real time data sourced from automated, telemetered monitoring buoys enables environmental managers to implement proactive algal bloom management strategies.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/35628