Estuarine flood modelling using artificial neural networks

Fazel, SAA; Blumenstein, M; Mirfenderesk, H; Tomlinson, R

Estuarine flood modelling using artificial neural networks

Fazel, SAA Blumenstein, M

Mirfenderesk, H Tomlinson, R

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 631 - 637
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Fazel, SAA	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.contributor.author	Mirfenderesk, H	en_US
dc.contributor.author	Tomlinson, R	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2014, pp. 631 - 637	en_US
dc.identifier.isbn	9781479914845	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121358
dc.description.abstract	© 2014 IEEE. Prediction of water levels at estuaries poses a significant challenge for modelling of floods due to the influence of tidal effects. In this study, a two-stage forecasting system is proposed. In the first stage, the tidal portion of the available records is used to develop a tidal prediction system. The predictions of the first stage are used for flood modelling in the second. Experimental results suggest that the proposed flood modelling approach is advantageous for forecasting flood levels with more than 1 hour lead times.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2014.6889704	en_US
dc.title	Estuarine flood modelling using artificial neural networks	en_US
dc.type	Conference Proceeding
utslib.for	0803 Computer Software	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/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. Prediction of water levels at estuaries poses a significant challenge for modelling of floods due to the influence of tidal effects. In this study, a two-stage forecasting system is proposed. In the first stage, the tidal portion of the available records is used to develop a tidal prediction system. The predictions of the first stage are used for flood modelling in the second. Experimental results suggest that the proposed flood modelling approach is advantageous for forecasting flood levels with more than 1 hour lead times.

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http://hdl.handle.net/10453/121358