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dc.contributor.authorFernando, Achela
dc.contributor.authorShamseldin, Asaad
dc.contributor.authorAbrahart, Robert
dc.date.accessioned2012-06-06T23:52:45Z
dc.date.available2013-01-20T20:45:16Z
dc.date.issued2012
dc.identifier.issn1084-0699
dc.identifier.urihttp://hdl.handle.net/10652/1887
dc.description.abstractThis paper deals with the application of an innovative method for combining estimated outputs from a number of rainfall-runoff models using Gene Expression Programming (GEP) to perform symbolic regression. The GEP multi-model combination method uses the synchronous simulated river flows from four conventional rainfall-runoff models to produce a set of combined river flow estimates for four different catchments. The four selected models for the multi-model combinations are the Linear Perturbation Model (LPM), the Linearly Varying Gain Factor Model (LVGFM), the Soil Moisture Accounting and Routing (SMAR) Model, and the Probability-Distributed Interacting Storage Capacity (PDISC) model. The first two of these models are ‘black-box’ models, the LPM exploiting seasonality and the LVGFM employing a storage-based coefficient of runoff. The remaining two are conceptual models. The data of four catchments with different geographical location, hydrological and climatic conditions, are used to test the performance of the GEP combination method. The results of the model using GEP method are compared against original forecasts obtained from the individual models that contributed to the development of the combined model by means of a few global statistics. The findings show that a GEP approach can successfully used as a multi-model combination method. In addition, the GEP combination method also has benefit over other hitherto tested approaches such as an artificial neural network combination method in that its formulation is transparent, can be expressed as a simple mathematical function, and therefore is useable by people who are unfamiliar with such advanced techniques. The GEP combination method is able to combine model outcomes from less accurate individual models and produce a superior river flow forecast.en_NZ
dc.language.isoenen_NZ
dc.publisherAmerican Society of Civil Engineersen_NZ
dc.relation.urihttp://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0000533en_NZ
dc.subjectRainfall-runoff modelen_NZ
dc.subjectSymbolic regressionen_NZ
dc.subjectModel combinationen_NZ
dc.subjectGene expression programmingen_NZ
dc.titleUse of gene expression programing for multi-model combination of rainfall-runoff modelsen_NZ
dc.typeJournal Articleen_NZ
dc.rights.holderAmerican Society of Civil Engineersen_NZ
dc.identifier.doihttp://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000533en_NZ
dc.subject.marsden090702 Environmental Engineering Modellingen_NZ
dc.identifier.bibliographicCitationFernando, A., Shamseldin, A., & Abrahart, R. (2011). Use of gene expression programing for multi-model combination of rainfall- runoff models. Journal of Hydrologic Engineering 17(9), 975–985. doi: http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000533en_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.institutionUniversity of Aucklanden_NZ
unitec.institutionUniversity of Nottinghamen_NZ
unitec.publication.spage975
unitec.publication.lpage985
unitec.publication.volume17
unitec.publication.titleJournal of Hydrologic Engineeringen_NZ
unitec.peerreviewedyesen_NZ


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