A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall-runoff models

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Title: A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall-runoff models
Authors: Shamseldin, Asaad Y.O'Connor, Kieran M.Nasr, Ahmed Elssidig
Permanent link: http://hdl.handle.net/10197/2421
Date: Oct-2007
Online since: 2010-08-23T16:00:39Z
Abstract: The performance of three artificial neural network (NN) methods for combining simulated river flows, based on three different neural network structures, are compared. These network structures are; the simple neural network (SNN), the radial basis function neural network (RBFNN) and the multi-layer perceptron neural network (MLPNN). Daily data of eight catchments, located in different parts of the world, and having different hydrological and climatic conditions, are used to enable comparisons of the performances of these three methods. In the case of each catchment, each neural network combination method synchronously uses the simulated river flows of four rainfall-runoff models operating in design non-updating mode to produce the combined river flows. Two of these four models are black-box, the other two being conceptual models. The results of the study show that the performances of all three combination methods are, on average, better than that of the best individual rainfall-runoff model utilized in the combination, i.e. that the combination concept works. In terms of the Nash-Sutcliffe R2 model efficiency index, the MLPNN combination method generally performs better than the other two combination methods tested. For most of the catchments, the differences in the R2 values of the SNN and the RBFNN combination methods are not significant but, on average, the SNN form performs marginally better than the more complex RBFNN alternative. Based on the results obtained for the three NN combination methods, the use of the multi-layer perceptron neural network (MLPNN) is recommended as the appropriate NN form for use in the context of combining simulated river flows.
Funding Details: Not applicable
Type of material: Journal Article
Publisher: Taylor & Francis
Journal: Hydrological Sciences Journal-Journal Des Sciences Hydrologiques
Volume: 52
Issue: 5
Start page: 896
End page: 916
Copyright (published version): 2007 IAHS Press
Keywords: Neural network combination methodsRainfall-runoff modelRiver flow forecastingMulti-layer perceptronRadial basis functionSimple neural network
Subject LCSH: Neural networks (Computer science)
Runoff--Computer programs
Streamflow--Forecasting
DOI: 10.1623/hysj.52.5.896
Other versions: http://dx.doi.org/10.1623/hysj.52.5.896
Language: en
Status of Item: Peer reviewed
Appears in Collections:Critical Infrastructure Group Research Collection
Civil Engineering Research Collection

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