Shamseldin, Asaad Y.Asaad Y.ShamseldinO'Connor, Kieran M.Kieran M.O'ConnorNasr, Ahmed ElssidigAhmed ElssidigNasr2010-08-232010-08-232007 IAHS2007-10Hydrological Sciences Journal-Journal Des Sciences Hydrologiques2150-3435 (electronic)0262-6667 (paper)http://hdl.handle.net/10197/2421The 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.671015 bytesapplication/pdfenNeural network combination methodsRainfall-runoff modelRiver flow forecastingMulti-layer perceptronRadial basis functionSimple neural networkNeural networks (Computer science)Runoff--Computer programsStreamflow--ForecastingJournal Article52589691610.1623/hysj.52.5.896https://creativecommons.org/licenses/by-nc-sa/1.0/