Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Estimation of Discharge in Rivers using ANN Modeled Rating Curves
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.
Estimation of Discharge in Rivers using ANN Modeled Rating Curves
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.
Estimation of Discharge in Rivers using ANN Modeled Rating Curves
Bakshi, S. (Autor:in) / Bhar, K. K. (Autor:in)
Journal of The Institution of Engineers (India): Series A ; 93 ; 181-186
01.09.2012
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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