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Estimation of Discharge in Rivers using ANN Modeled Rating Curves

Estimation of Discharge in Rivers using ANN Modeled Rating Curves Rating curves, commonly used for estimation of discharge in a river are usually steady-flow rating curves, in which discharge is expressed as a function of stage only. However, these rating curves may be inadequate due to a number of factors like backwater effect, changes in cross-section from scouring or silting, rapidly changing discharge etc. Problems with variable backwater and unsteady flow are usually dealt by including additional parameters like water surface slope in the stage–discharge relation. But these approaches have limitations due to absence of other parameters governing flow conditions in the model. In these cases, artificial neural network (ANN) can be used effectively as an alternative. Models with multiple input variables can be developed for prediction of discharge with much more accuracy. In this work, a number of ANN models have been developed for estimation of discharge. Stage and discharge data from a river reach with a bridge crossing were used for training, validation and simulation of the ANN models. Results show that the neural networks are capable of predicting the discharges under varying flow conditions. Loops in rating curves and backwater effect are estimated quite accurately by multiple-input ANN models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India): Series A Springer Journals

Estimation of Discharge in Rivers using ANN Modeled Rating Curves

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References (13)

Publisher
Springer Journals
Copyright
Copyright © 2013 by The Institution of Engineers (India)
Subject
Engineering; Civil Engineering
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-013-0020-4
Publisher site
See Article on Publisher Site

Abstract

Rating curves, commonly used for estimation of discharge in a river are usually steady-flow rating curves, in which discharge is expressed as a function of stage only. However, these rating curves may be inadequate due to a number of factors like backwater effect, changes in cross-section from scouring or silting, rapidly changing discharge etc. Problems with variable backwater and unsteady flow are usually dealt by including additional parameters like water surface slope in the stage–discharge relation. But these approaches have limitations due to absence of other parameters governing flow conditions in the model. In these cases, artificial neural network (ANN) can be used effectively as an alternative. Models with multiple input variables can be developed for prediction of discharge with much more accuracy. In this work, a number of ANN models have been developed for estimation of discharge. Stage and discharge data from a river reach with a bridge crossing were used for training, validation and simulation of the ANN models. Results show that the neural networks are capable of predicting the discharges under varying flow conditions. Loops in rating curves and backwater effect are estimated quite accurately by multiple-input ANN models.

Journal

Journal of The Institution of Engineers (India): Series A – Springer Journals

Published: Mar 6, 2013

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