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Modeling River Stage‐Discharge Relationships Using Different Neural Network Computing Techniques
Author(s) -
Kisi Özgür,
Çobaner Murat
Publication year - 2009
Publication title -
clean – soil, air, water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.444
H-Index - 66
eISSN - 1863-0669
pISSN - 1863-0650
DOI - 10.1002/clen.200800010
Subject(s) - artificial neural network , rating curve , stage (stratigraphy) , perceptron , mean squared error , computer science , regression , multilayer perceptron , basis (linear algebra) , discharge , linear regression , artificial intelligence , data mining , statistics , hydrology (agriculture) , algorithm , mathematics , machine learning , geology , geography , cartography , geotechnical engineering , paleontology , geometry , sediment , drainage basin
One of the most important problems in hydrology is the establishment of rating curves. The statistical tools that are commonly used for river stage‐discharge relationships are regression and curve fitting. However, these techniques are not adequate in view of the complexity of the problems involved. Three different neural network techniques, i. e., multi‐layer perceptron neural network with Levenberg‐Marquardt and quasi‐Newton algorithms and radial basis neural networks, are used for the development of river stage‐discharge relationships by constructing nonlinear relationships between stage and discharge. Daily stage and flow data from three stations, Yamula, Tuzkoy and Sogutluhan, on the Kizilirmak River in Turkey were used. Regression techniques are also applied to the same data. Different input combinations including the previous stages and discharges are used. The models' results are compared using three criteria, i. e., root mean square errors, mean absolute error and the determination coefficient. The results of the comparison reveal that the neural network techniques are much more suitable for setting up stage‐discharge relationships than the regression techniques. Among the neural network methods, the radial basis neural network is found to be slightly better than the others.