Abstract
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. It was seen that all three different ANN algorithms compared well with conventional multi linear regression (MLR) technique. It was seen that only GRNN technique did not provide negative flow estimations for some observations. The rainfall-runoff correlogram was successfully used in determination of the input layer node number.
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© 2004 Springer-Verlag Berlin Heidelberg
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Cigizoglu, H.K., Alp, M. (2004). Rainfall-Runoff Modelling Using Three Neural Network Methods. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_20
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DOI: https://doi.org/10.1007/978-3-540-24844-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
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