Open AccessComparison of different methods for developing a stage–discharge curve of the Kizilirmak River
Author(s) -
Hasanpour Kashani M.,
Daneshfaraz R.,
Ghorbani M.A.,
Najafi M.R.,
Kisi O.
Publication year - 2015
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12064
Subject(s) - stage (stratigraphy) , rating curve , statistics , mean squared error , correlation coefficient , coefficient of determination , mathematics , hydrology (agriculture) , computer science , biology , geology , geotechnical engineering , paleontology , sediment
Prediction of a stage–discharge relationship is of immense importance for reliable planning, design and management of most water resources projects. The aim of this study is to compare the performance of artificial intelligence methods, namely, A rtificial N eural N etwork, A daptive N ero‐ F uzzy I nference S ystem ( ANFIS ), and G ene‐ E xpression P rogramming, with those of two conventional methods, i.e., stage‐rating curve and R egression techniques in deriving a stage–discharge curve in the K izilirmak R iver, T urkey. The daily minimum, mean and maximum river discharge, and stage data for period of 2005–2007 were used for training and testing the models. The comparison includes visual and parametric approaches, namely coefficient of correlation, mean absolute error and root mean square error. The results proved the high ability of the artificial intelligence methods in developing stage–discharge relationship. Furthermore, the performance of the ANFIS model was found to be superior to all the models.