An application of artificial intelligence for rainfall–runoff modeling

Journal of Earth system science

Ali Aytek1¹  Murat Alp2²  M Asce1¹ 

[1] Gaziantep University, Civil Engineering Department, Hydraulics Division, 27310 Gaziantep, Turkey.$$;State Hydraulics Works, 14 Regional Directorate, 34696 Küçükçamlıca, Istanbul, Turkey.$$

关键词: Artificial intelligence;    artificial neural networks;    evolutionary computation;    genetic programming;    gene expression programming;    rainfall;    runoff.;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

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 (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.

【 授权许可】

Unknown