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dc.contributor.authorJoorabchi, Amirhassanen_US
dc.contributor.authorZhang, Hongen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.date.accessioned2017-04-24T12:28:02Z
dc.date.available2017-04-24T12:28:02Z
dc.date.issued2009en_US
dc.date.modified2010-05-18T06:42:01Z
dc.identifier.issn07490208en_US
dc.identifier.doihttp://e-geo.fcsh.unl.pt/ICS2009/jcr_si56.htmlen_AU
dc.identifier.urihttp://hdl.handle.net/10072/29721
dc.description.abstractIn the present study, Artificial Neural Networks (ANNs) are adopted to simulate groundwater table fluctuations. A multilayer feed-forward neural network model has been developed and trained using a back-propagation algorithm. The training data was based on field measurements (KANG et al., 1994) from five different locations down the east coast of Australia. The data included information on watertable, tide elevation, beach slopes and hydraulic conductivity at each beach. The results from the developed model show that the artificial neural network model is very successful in terms of the prediction of a target that is dependent on a number of variables. Sensitivity analysis was undertaken which confirmed that a variation in tide elevation is the most important parameter to use for simulating groundwater levels in coastal aquifers.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.format.extent782014 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherCoastal Education & Research Foundationen_US
dc.publisher.placeUnited Statesen_US
dc.publisher.urihttp://www.cerf-jcr.org/en_AU
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofpagefrom966en_US
dc.relation.ispartofpageto970en_US
dc.relation.ispartofissue2en_US
dc.relation.ispartofjournalJournal of Coastal Researchen_US
dc.relation.ispartofvolumeSI 56en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchWater Resources Engineeringen_US
dc.subject.fieldofresearchSimulation and Modellingen_US
dc.subject.fieldofresearchcode090509en_US
dc.subject.fieldofresearchcode080110en_US
dc.titleApplication of artificial neural networks to groundwater dynamics in coastal aquifersen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.rights.copyrightCopyright 2009 CERF. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.en_AU
gro.date.issued2009
gro.hasfulltextFull Text


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