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dc.contributor.convenorJohn Lawrenceen_US
dc.contributor.authorRichards, Russellen_US
dc.contributor.authorTomlinson, Rodgeren_US
dc.contributor.authorChaloupka, Milanien_US
dc.contributor.editorDavid A. Swayne, Wanhong Yang, A. A. Voinov, A. Rizzoli, T. Filatovaen_US
dc.date.accessioned2017-05-03T12:51:24Z
dc.date.available2017-05-03T12:51:24Z
dc.date.issued2010en_US
dc.date.modified2013-05-29T03:06:03Z
dc.identifier.refurihttp://www.iemss.org/iemss2010/en_US
dc.identifier.urihttp://hdl.handle.net/10072/35922
dc.description.abstractAn on-going challenge for decision makers is the interpretation of temporal trends from monitoring data given that environmental processes often generate complex data that are multivariate and potentially nonlinear. Generalized additive models (GAMs) is a well-suited modelling framework for uncovering such trends and unifying datasets. This approach allows flexible specification of regression splines to represent the functional relationships between a response variable (the parameter of interest) and a suite of temporal and spatial covariates that can be continuous or discrete using a link function and smooth functions of the covariates. We highlight the utility of using GAMs through three case studies. The first highlights the use of a GAM to unify the findings of an established longterm water quality-monitoring program with those of a focused short-term monitoring program. In the second, a GAM is used to evaluate the spatial patterns in a biomonitoring dataset whilst simultaneously accounting for variability in oyster size, which can have a confounding effect on such data. The final case study focuses on a 12 month continuous monitoring program of oceanographic data as part of an evaluation of the environmental conditions for a desalination plant intake pipe. The context for these studies is predominantly water quality in the coastal zone, however the benefits and widespread application to other research areas is clearly evident.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent781082 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisheriEMSsen_US
dc.publisher.placeOttawaen_US
dc.publisher.urihttp://www.iemss.org/iemss2010/en_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencenameInternational Environmental Modelling and Software Society (iEMSs) 2010 International Congress on Enen_US
dc.relation.ispartofconferencetitleProceedings of the iEMSs Fifth Biennial Meeting: International Congress on Environmental Modelling and Software (iEMSs 2010)en_US
dc.relation.ispartofdatefrom2010-07-05en_US
dc.relation.ispartofdateto2010-07-08en_US
dc.relation.ispartoflocationOttawa, Canadaen_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchEnvironmental Monitoringen_US
dc.subject.fieldofresearchEnvironmental Science and Management not elsewhere classifieden_US
dc.subject.fieldofresearchcode050206en_US
dc.subject.fieldofresearchcode050299en_US
dc.titleUsing Generalized Additive Models to Assess, Explore and Unify Environmental Monitoring Datasetsen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, Griffith School of Environmenten_US
gro.rights.copyrightCopyright remains with the authors 2010. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference’s website or contact the authors.en_US
gro.date.issued2010
gro.hasfulltextFull Text


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    Contains papers delivered by Griffith authors at national and international conferences.

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