Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecast combination

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dc.contributor.author Shamseldin, Asaad Y.
dc.contributor.author Nasr, Ahmed Elssidig
dc.contributor.author O'Connor, Kieran M.
dc.date.accessioned 2010-07-30T13:53:54Z
dc.date.available 2010-07-30T13:53:54Z
dc.date.copyright 2002 author(s) en
dc.date.issued 2002-10
dc.identifier.citation Hydrology and Earth System Sciences en
dc.identifier.issn 1812-2108
dc.identifier.uri http://hdl.handle.net/10197/2274
dc.description.abstract The multi-layer feed-forward neural network (MLFFNN) is applied in the context of river flow forecast combination, where a number of rainfall-runoff models are used simultaneously to produce an overall combined river flow forecast. The operation of the MLFFNN depends on the neuron transfer function, which is non-linear. These models, each having a different structure to simulate the perceived mechanisms of the runoff process, utilise the information carrying capacity of the model calibration data indifferent ways. Hence, in a discharge forecast combination procedure, the discharge forecasts of each model provide a source of information different from that of the other models used in the combination. In the present work, the significance of the choice of the transfer function type in the overall performance of the MLFFNN, when used in the river flow forecast combination context is critically investigated. Five neuron transfer functions are used in this investigation, namely, the logistic function, the bipolar function, the hyperbolic function, the arctan function and the scaled arctan function. The results indicate that the logistic function yields the best model forecast combination performance. en
dc.description.sponsorship Not applicable en
dc.format.extent 699704 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher European Geosciences Union en
dc.relation.requires Critical Infrastructure Group Research Collection en
dc.rights This work is licensed under a Creative Commons License en
dc.subject River flow forecast combination en
dc.subject Multi-layer feed-forward neural network en
dc.subject Neuron transfer functions en
dc.subject Rainfall-runoff models en
dc.subject.lcsh Streamflow--Forecasting en
dc.subject.lcsh Neural networks (Computer science) en
dc.subject.lcsh Runoff--Computer programs en
dc.title Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecast combination en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.internal.webversions Publisher's version en
dc.internal.webversions http://dx.doi.org/10.5194/hess-6-671-2002 en
dc.status Peer reviewed en
dc.identifier.volume 6 en
dc.identifier.issue 4 en
dc.identifier.startpage 671 en
dc.identifier.endpage 684 en
dc.identifier.doi 10.5194/hess-6-671-2002
dc.neeo.contributor Shamseldin|Asaad Y.|aut| en
dc.neeo.contributor Nasr|Ahmed Elssidig|aut| en
dc.neeo.contributor O'Connor|Kieran M.|aut| en
dc.description.admin ti vo is st en SB. 20/7/10 en


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