| Authors: |
Ali Aytek
and M Asce
and Murat Alp
|
| URL: |
http://www.ias.ac.in/jess/apr2008/d093.pdf |
| Tags: |
Expression
Gene
Programming
algorithms,
genetic
programming,
|
| 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. |
@article{Aytek:2008:JESS,
title = {An application of artificial intelligence for
rainfall-runoff modeling},
author = {Ali Aytek and M Asce and Murat Alp},
journal = {Journal of Earth System Science},
month = {April},
number = {2},
pages = {145--155},
url = {http://www.ias.ac.in/jess/apr2008/d093.pdf},
volume = {117},
year = {2008},
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.},
size = {11 pages}, email = {aytek@gantep.edu.tr},
keywords = {Expression Gene Programming algorithms, genetic programming, }
}
::