A Neutral Network Approach to Rainfall Forecasting in urban environments

Ball, JE; Luk, KC

A Neutral Network Approach to Rainfall Forecasting in urban environments

Ball, JE

Luk, KC

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Publisher:: AA Balkema Publishers
Publication Type:: Chapter
Citation:: Neural networks for hydrological modelling, 2004, 1, pp. 177 - 195
Issue Date:: 2004-01

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Field	Value	Language
dc.contributor.author	Ball, JE https://orcid.org/0000-0001-8342-1916	en_US
dc.contributor.author	Luk, KC	en_US
dc.contributor.editor	Abrahart, RJ	en_US
dc.contributor.editor	Kneale, PE	en_US
dc.contributor.editor	See, LM	en_US
dc.date.issued	2004-01	en_US
dc.identifier.citation	Neural networks for hydrological modelling, 2004, 1, pp. 177 - 195	en_US
dc.identifier.isbn	90-5809-619-x	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7886
dc.description.abstract	An effective flood warning system in urban areas must provide the warnign with sufficient lead time for an appropriate response by the relevant emergency services and the affected community. The requirement poses a critical problem as most urban catchments are characterised by a fast hydrologic response to storm events. The approach used here to forecast rainfall over the Upper Paramatta River Catchment in Sydney is based on the application of a pattern recognition technique using an artificial neural network. It assumes that the future rainfall is a function of a discrete number of past spatial and temporal rainfall records; an important task, therefore, is the determination of the number of spatial and temporal rainfall records necessary for accurate prediction of future rainfall. The rainfall prediction model performed best when an optimal amount of spatial and temporal rainfall information was provided to the network.	en_US
dc.publisher	AA Balkema Publishers	en_US
dc.relation.ispartof	Neural networks for hydrological modelling	en_US
dc.title	A Neutral Network Approach to Rainfall Forecasting in urban environments	en_US
dc.type	Chapter
utslib.location	Leiden, The Netherlands	en_US
utslib.for	040608 Surfacewater Hydrology	en_US
utslib.for	090509 Water Resources Engineering	en_US
utslib.citation.edition	1	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.consider-herdc	false	en_US
pubs.edition	1	en_US
pubs.place-of-publication	Leiden, The Netherlands	en_US

Abstract:

An effective flood warning system in urban areas must provide the warnign with sufficient lead time for an appropriate response by the relevant emergency services and the affected community. The requirement poses a critical problem as most urban catchments are characterised by a fast hydrologic response to storm events. The approach used here to forecast rainfall over the Upper Paramatta River Catchment in Sydney is based on the application of a pattern recognition technique using an artificial neural network. It assumes that the future rainfall is a function of a discrete number of past spatial and temporal rainfall records; an important task, therefore, is the determination of the number of spatial and temporal rainfall records necessary for accurate prediction of future rainfall. The rainfall prediction model performed best when an optimal amount of spatial and temporal rainfall information was provided to the network.

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http://hdl.handle.net/10453/7886