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Use of gene expression programing for multi-model combination of rainfall-runoff models

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Show simple record Fernando, Achela Shamseldin, Asaad Abrahart, Robert 2012-06-06T23:52:45Z 2013-01-20T20:45:16Z 2012
dc.identifier.issn 1084-0699
dc.description.abstract This 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.iso en en_NZ
dc.publisher American Society of Civil Engineers en_NZ
dc.relation.uri en_NZ
dc.subject Rainfall-runoff model en_NZ
dc.subject Symbolic regression en_NZ
dc.subject Model combination en_NZ
dc.subject Gene expression programming en_NZ
dc.title Use of gene expression programing for multi-model combination of rainfall-runoff models en_NZ
dc.type Journal Article en_NZ
dc.rights.holder American Society of Civil Engineers en_NZ
dc.identifier.doi en_NZ
dc.subject.marsden 090702 Environmental Engineering Modelling en_NZ
dc.identifier.bibliographicCitation Fernando, 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: en_NZ
unitec.institution Unitec Institute of Technology en_NZ
unitec.institution University of Auckland en_NZ
unitec.institution University of Nottingham en_NZ
unitec.publication.spage 975
unitec.publication.lpage 985
unitec.publication.volume 17
unitec.publication.title Journal of Hydrologic Engineering en_NZ
unitec.peerreviewed yes en_NZ

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