Prediciting Algal Blooms in the Berowra Estuary, NSW, Australia

Coad, P; Ball, JE; Cathers, B; van Senden, D

Prediciting Algal Blooms in the Berowra Estuary, NSW, Australia

Coad, P Ball, JE

Cathers, B van Senden, D

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Publisher:: TuTech Verlag - TuTech Innovation GmbH
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 10th International Conference on Hydroinformatics "Understanding Changing Climate and Environment and Finding Solutions", 2012, pp. 1 - 8
Issue Date:: 2012-01

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Field	Value	Language
dc.contributor.author	Coad, P	en_US
dc.contributor.author	Ball, JE https://orcid.org/0000-0001-8342-1916	en_US
dc.contributor.author	Cathers, B	en_US
dc.contributor.author	van Senden, D	en_US
dc.contributor.editor	Hinkelmann, R	en_US
dc.contributor.editor	Nasermoaddeli, MH	en_US
dc.contributor.editor	Liong, SY	en_US
dc.contributor.editor	Savic, D	en_US
dc.contributor.editor	Frehle, P	en_US
dc.contributor.editor	Daemrich, KF	en_US
dc.date	2012-07-14	en_US
dc.date.issued	2012-01	en_US
dc.identifier.citation	Proceedings of the 10th International Conference on Hydroinformatics "Understanding Changing Climate and Environment and Finding Solutions", 2012, pp. 1 - 8	en_US
dc.identifier.isbn	978-3-941492-45-5	en_US
dc.identifier.uri	http://hdl.handle.net/10453/31187
dc.description.abstract	The Berowra Creek estuary and its catchment are situated entirely within the Hornsby Shire Local Government Area on the northern outskirts of the Sydney metropolitan area. Berowra Creek is a major tributary of the lower Hawkesbury River, entering the Hawkesbury River some 25 kilometres from the ocean. The estuary itself extends for over 23 kilometres in a southerly direction from the Hawkesbury River to the tidal limit at Rocky Fall Rapids. Algal blooms are prevalent within the Berowra Estuary. When algal species are present in high numbers they pose serious problems for commercial and recreational users of the estuary. Management authorities require an understanding of the relationship between the incidence of algal blooms and the environmental conditions required to initiate and promote these populations. Artificial Neural Networks (ANNs) have been developed to predict the occurrence and risk of algal blooms within the Berowra Estuary. The ANNs developed were of a multilayer perceptron architecture and used the Broyden-Fletcher-Goldfarb-Shanno training algorithm. To enable the prediction of algal blooms, an instrumented buoy was deployed at Calabash Bay within the estuary. This buoy records temperature, salinity, photosynthetically available radiation and chlorophyll-a (CHLa). The ANNs were used to predict CHLa concentrations from one to seven days in advance. Presented in the paper will be an assessment of the prediction reliability together with an assessment of how the variables and the information pre-processing influences the reliability of the predictions obtained.	en_US
dc.publisher	TuTech Verlag - TuTech Innovation GmbH	en_US
dc.relation.ispartof	Proceedings of the 10th International Conference on Hydroinformatics "Understanding Changing Climate and Environment and Finding Solutions"	en_US
dc.relation.ispartof	International Conference on Hydroinformatics	en_US
dc.title	Prediciting Algal Blooms in the Berowra Estuary, NSW, Australia	en_US
dc.type	Conference Proceeding
utslib.location	Germany	en_US
utslib.location.activity	Hamburg, Germany	en_US
utslib.for	0905 Civil Engineering	en_US
dc.location.activity	Hamburg, Germany
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.consider-herdc	true	en_US
pubs.finish-date	2012-07-18	en_US
pubs.place-of-publication	Germany	en_US
pubs.start-date	2012-07-14	en_US

Abstract:

The Berowra Creek estuary and its catchment are situated entirely within the Hornsby Shire Local Government Area on the northern outskirts of the Sydney metropolitan area. Berowra Creek is a major tributary of the lower Hawkesbury River, entering the Hawkesbury River some 25 kilometres from the ocean. The estuary itself extends for over 23 kilometres in a southerly direction from the Hawkesbury River to the tidal limit at Rocky Fall Rapids. Algal blooms are prevalent within the Berowra Estuary. When algal species are present in high numbers they pose serious problems for commercial and recreational users of the estuary. Management authorities require an understanding of the relationship between the incidence of algal blooms and the environmental conditions required to initiate and promote these populations. Artificial Neural Networks (ANNs) have been developed to predict the occurrence and risk of algal blooms within the Berowra Estuary. The ANNs developed were of a multilayer perceptron architecture and used the Broyden-Fletcher-Goldfarb-Shanno training algorithm. To enable the prediction of algal blooms, an instrumented buoy was deployed at Calabash Bay within the estuary. This buoy records temperature, salinity, photosynthetically available radiation and chlorophyll-a (CHLa). The ANNs were used to predict CHLa concentrations from one to seven days in advance. Presented in the paper will be an assessment of the prediction reliability together with an assessment of how the variables and the information pre-processing influences the reliability of the predictions obtained.

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http://hdl.handle.net/10453/31187