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dc.contributor.authorFernando, Achela
dc.contributor.authorShamseldin, Asaad
dc.description.abstractThis paper deals with the challenging problem of hydrological interpretation of the internal functioning of ANNs by extracting knowledge from their solutions. The neural network used in this study is based on the structure of the Radial Basis Function Neural Network (RBFNN) which is considered as an alternative to the Multi Layer Perceptron (MLPNN) for solving complex modelling problems. This network consists of an input, hidden and an output layer. The network is trained using the daily data of two catchments having different characteristics and from two different regions in the world. The present day and antecedent observed discharges are used as inputs to the network to forecast the flow one day ahead. A range of quantitative and qualitative techniques are used for hydrological interpretation of the internal functioning by examining the responses of the hidden layer neurons. The results of the study show that a single hidden layered RBFNN is an effective tool to forecast the daily flows and that the activation of the hidden layer nodes are far from arbitrary but appear to represent flow components of the predicted hydrograph. The results of the study confirm that the three neurons in the hidden layer of this model effectively divide the input data space in such a way that the contribution from each neurone dominates in one of the flow domains – low, medium or high – and form, in a crude manner, the base flow, interflow and surface runoff components of the hydrograph.en_NZ
dc.publisherAmerican Society of Civil Engineersen_NZ
dc.rightsAll rights reserveden_NZ
dc.subjecthydrological interpretationen_NZ
dc.subjecthidden neuronsen_NZ
dc.subjectradial basis functionen_NZ
dc.subjectArtificial Neural Network (ANN)en_NZ
dc.titleInvestigation of the internal functioning of the radial basis function neural network river flow forecasting modelsen_NZ
dc.typeJournal Articleen_NZ
dc.rights.holderAmerican Society of Civil Engineersen_NZ
dc.subject.marsden091501 Computational Fluid Dynamicsen_NZ
dc.identifier.bibliographicCitationFernando, D.A.K., & Shamseldin, A.Y. (2009). Investigation of the internal functioning of the radial basis function neural network river flow forecasting models. Journal of Hydrologic Engineering, 14(3), 286-292. doi: 10.1061/(ASCE)1084-0699(2009)14:3(286)en_NZ
unitec.institutionUnitec Institute of Technologyen_NZ
unitec.institutionUniversity of Aucklanden_NZ
unitec.publication.titleJournal of Hydrologic Engineeringen_NZ
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
unitec.institution.studyareaConstruction + Engineering

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