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contributor authorK. P. Sudheer
contributor authorS. K. Jain
date accessioned2017-05-08T21:23:36Z
date available2017-05-08T21:23:36Z
date copyrightMay 2003
date issued2003
identifier other%28asce%291084-0699%282003%298%3A3%28161%29.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/49712
description abstractThe establishment of a rating curve is an important problem in hydrology. Generally, a regression approach is applied to establish the relationship between stage and discharge. However, this approach fails in the cases where hysteresis is present in the data. The aim of the study is to investigate the potential of employing radial basis function (RBF) type neural networks for modeling stage-discharge relationships at gauging stations and to compare different types of networks. The results are promising and suggest that the neural network approach is highly viable. A comparison of the RBF models with backpropagation type neural networks reveals that the former is superior in performance for rating curves exhibiting hysteresis.
publisherAmerican Society of Civil Engineers
titleRadial Basis Function Neural Network for Modeling Rating Curves
typeJournal Paper
journal volume8
journal issue3
journal titleJournal of Hydrologic Engineering
identifier doi10.1061/(ASCE)1084-0699(2003)8:3(161)
treeJournal of Hydrologic Engineering:;2003:;Volume ( 008 ):;issue: 003
contenttypeFulltext


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