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dc.contributor.authorFernando, Achela
dc.contributor.authorAbrahart, Robert
dc.contributor.authorShamseldin, Asaad
dc.date.accessioned2012-06-10T23:20:25Z
dc.date.available2012-06-10T23:20:25Z
dc.date.issued2009
dc.identifier.issn1607-7962
dc.identifier.otherEGU2009-13886
dc.identifier.urihttps://hdl.handle.net/10652/1888
dc.description.abstractTwo previous studies have evaluated eight multi-model forecasting strategies that combined hydrological forecasts for contrasting catchments: the River Ouse in Northern England and the Upper River Wye in Central Wales. The level and discharge inputs that were combined comprised a mixed set of independent forecasts produced using different modelling methodologies. Earlier multi-model combination approaches comprised: arithmetic-averaging, a probabilistic method in which the best model from the last time step is used to generate the current forecast, two different neural network operations, two different soft computing methodologies, a regression tree solution and instance-based learning. The nature and properties of past combination functions was not however explored and no theoretical outcome to support subsequent improvements resulted. This paper presents a pair of counterpart mathematical equations that were evolved in GeneXproTools 4.0: a powerful software package that is used to perform symbolic regression operations using gene expression programming. The results suggest that simple mathematical equations can be used to perform efficacious multi-model combinations; that similar mathematical solutions can be developed to fulfil different hydrological modelling requirements; and that the procedure involved produces mathematical outcomes that can be explained in terms of minimalist problem-solving strategies.en_NZ
dc.language.isoenen_NZ
dc.publisherCopernicus Publicationsen_NZ
dc.relation.urihttp://meetingorganizer.copernicus.org/EGU2009/EGU2009-13886.pdfen_NZ
dc.subjectrainfall-runoff modelen_NZ
dc.subjectgene expression programmingen_NZ
dc.subjecthydrological modellingen_NZ
dc.titleMulti-model forecasting: Using gene expression programming to develop explicit equations for rainfall-runoff modelling combinationsen_NZ
dc.typeConference Contribution - Oral Presentationen_NZ
dc.rights.holderAuthorsen_NZ
dc.subject.marsden090702 Environmental Engineering Modellingen_NZ
dc.identifier.bibliographicCitationAbrahart, R.J., Shamseldin, A.Y., & Fernando, D.A.K. (2009). Multi-model forecasting: Using gene expression programming to develop explicit equations for rainfall-runoff modelling combinations [Abstract]. Geophysical Research Abstracts, 11, EGU2009-13886. Available from http://meetingorganizer.copernicus.org/EGU2009/EGU2009-13886.pdfen_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.institutionUniversity of Aucklanden_NZ
unitec.institutionNottingham Universityen_NZ
unitec.publication.volume11en_NZ
unitec.publication.titleGeophysical Research Abstractsen_NZ
unitec.conference.titleEuropean Geosciences Union General Assembly 2009en_NZ
unitec.conference.orgEuropean Geosciences Unionen_NZ
unitec.conference.locationViennaen_NZ
unitec.conference.sdate2009-04-19
unitec.conference.edate2009-04-24
unitec.peerreviewedyesen_NZ
unitec.identifier.roms43367


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