An application of artificial intelligence for rainfall-runoff modeling |
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| Authors: | Ali Aytek M Asce Murat Alp |
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| Institution: | 1.Civil Engineering Department, Hydraulics Division,Gaziantep University,Gaziantep,Turkey;2.State Hydraulics Works,Kü?ük?aml?ca, Istanbul,Turkey |
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| Abstract: | This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modeling: the artificial
neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation
(FFBP) and generalized regression neural network (GRNN) methods are compared with one EC method, Gene Expression Programming
(GEP) which is a new evolutionary algorithm that evolves computer programs. The daily hydrometeorological data of three rainfall
stations and one streamflow station for Juniata River Basin in Pennsylvania state of USA are taken into consideration in the
model development. Statistical parameters such as average, standard deviation, coefficient of variation, skewness, minimum
and maximum values, as well as criteria such as mean square error (MSE) and determination coefficient (R
2) are used to measure the performance of the models. The results indicate that the proposed genetic programming (GP) formulation
performs quite well compared to results obtained by ANNs and is quite practical for use. It is concluded from the results
that GEP can be proposed as an alternative to ANN models. |
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| Keywords: | Artificial intelligence artificial neural networks evolutionary computation genetic programming gene expression programming rainfall runoff |
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