Three neural network methods, feed forward back propagation (FFBP), radial basis function (RBF) and generalized regression neural network (GRNN) were employed for rainfall-runoff modelling of Turkish hydrometeorologic data. The daily rainfall and daily mean flow data are coupled to form the basis of rainfall-flow modelling using different ANN configurations. It was seen that all three different ANN algorithms compared well with conventional multi linear regression (MLR) technique. The peak flows of the observed hydrographs were closer approximated by FFBP and RBF algorithms. It was seen that only GRNN technique did not provide negative flow estimations for some observations. The rainfall-flow correlogram was successfully used in order to determine the input layer structure of the ANN configurations
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