%0 Journal Article %1 Aytek:2008:JESS %A Aytek, Ali %A Asce, M %A Alp, Murat %D 2008 %J Journal of Earth System Science %K Expression Gene Programming algorithms, genetic programming, %N 2 %P 145--155 %T An application of artificial intelligence for rainfall-runoff modeling %U http://www.ias.ac.in/jess/apr2008/d093.pdf %V 117 %X This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modelling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalised 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 (R2) 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.

@article{Aytek:2008:JESS, abstract = {This study proposes an application of two techniques of artificial intelligence (AI) for rainfall-runoff modelling: the artificial neural networks (ANN) and the evolutionary computation (EC). Two different ANN techniques, the feed forward back propagation (FFBP) and generalised 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 (R2) 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.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Aytek, Ali and Asce, M and Alp, Murat}, biburl = {https://www.bibsonomy.org/bibtex/2eea2123852d217d1dead3c32c6ce34b7/brazovayeye}, email = {aytek@gantep.edu.tr}, interhash = {cf9a4c0d6b802655d39a563a012540cc}, intrahash = {eea2123852d217d1dead3c32c6ce34b7}, journal = {Journal of Earth System Science}, keywords = {Expression Gene Programming algorithms, genetic programming,}, month = {April}, number = 2, pages = {145--155}, size = {11 pages}, timestamp = {2008-06-19T17:35:58.000+0200}, title = {An application of artificial intelligence for rainfall-runoff modeling}, url = {http://www.ias.ac.in/jess/apr2008/d093.pdf}, volume = 117, year = 2008 }