contributor author | K. P. Sudheer | |
contributor author | S. K. Jain | |
date accessioned | 2017-05-08T21:23:36Z | |
date available | 2017-05-08T21:23:36Z | |
date copyright | May 2003 | |
date issued | 2003 | |
identifier other | %28asce%291084-0699%282003%298%3A3%28161%29.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/49712 | |
description abstract | The 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. | |
publisher | American Society of Civil Engineers | |
title | Radial Basis Function Neural Network for Modeling Rating Curves | |
type | Journal Paper | |
journal volume | 8 | |
journal issue | 3 | |
journal title | Journal of Hydrologic Engineering | |
identifier doi | 10.1061/(ASCE)1084-0699(2003)8:3(161) | |
tree | Journal of Hydrologic Engineering:;2003:;Volume ( 008 ):;issue: 003 | |
contenttype | Fulltext | |