Linear genetic programming application for successive-station monthly streamflow prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{DanandehMehr:2014:CG,

• author = "Ali {Danandeh Mehr} and Ercan Kahya and Cahit Yerdelen",
• title = "Linear genetic programming application for successive-station monthly streamflow prediction",
• journal = "Computer \& Geosciences",
• volume = "70",
• pages = "63--72",
• year = "2014",
• ISSN = "0098-3004",
• DOI = "doi:10.1016/j.cageo.2014.04.015",
• URL = "http://www.sciencedirect.com/science/article/pii/S0098300414001010",
• abstract = "In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalised regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Coruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were used to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.",
• keywords = "genetic algorithms, genetic programming, Artificial neural networks, Streamflow prediction, Successive stations",

}

Genetic Programming entries for Ali Danandeh Mehr Ercan Kahya Cahit Yerdelen

Citations