Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecast combination
|Title:||Comparison of different forms of the multi-layer feed-forward neural network method used for river flow forecast combination||Authors:||Shamseldin, Asaad Y.
Nasr, Ahmed Elssidig
O'Connor, Kieran M.
|Permanent link:||http://hdl.handle.net/10197/2274||Date:||Oct-2002||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.||Funding Details:||Not applicable||Type of material:||Journal Article||Publisher:||European Geosciences Union||Journal:||Hydrology and Earth System Sciences||Volume:||6||Issue:||4||Start page:||671||End page:||684||Copyright (published version):||2002 author(s)||Keywords:||River flow forecast combination; Multi-layer feed-forward neural network; Neuron transfer functions; Rainfall-runoff models||Subject LCSH:||Streamflow--Forecasting
Neural networks (Computer science)
|DOI:||10.5194/hess-6-671-2002||Other versions:||http://dx.doi.org/10.5194/hess-6-671-2002||Language:||en||Status of Item:||Peer reviewed||metadata.dc.date.available:||2010-07-30T13:53:54Z|
|Appears in Collections:||Critical Infrastructure Group Research Collection|
Civil Engineering Research Collection
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