IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Sediment management and morphodynamics : Integrating artificial neural networks into hydromorphological model for fluvial channels
Integrating artificial neural networks into hydromorphological model for fluvial channels
One of the weakest points of hydromorphological models is to use empirical formulae for calculating sediment transport
rates, which are of limited generality. In many cases, unreasonable morphological changes are predicted and the results
of the different formulae often vary strongly. The reasons are assumed in the complexity of the interaction between flow
and sediment transport and in limitations of the nonlinear regression applied in these methods. In contrast to most
traditional empirical methods, which need prior knowledge about the nature of the relationships among the data, the
methods of artificial neural networks (ANN) learn from data examples presented to them in order to capture the subtle
functional relationships among the data even if the underlying relationships are unknown or the physical meaning is
difficult to explain. Additionally ANN has proven a high tolerance against data sample errors. These attributes make the
utilization of ANN for sediment transport predictions very promising. In this paper an optimal ANN model has been
selected, which is integrated into a hydromorphological model system and could adequately predict the morphological
changes in a straight alluvial channel under steady flow discharge. For this purpose the capability and accuracy of
numerous ANN models designed with different structures and trained with different learning rules has been analyzed. To
evaluate the prediction qualities of the designed networks, a comparative study has been carried out for these models by
evaluating several statistical parameters that describe the errors associated with the model in terms of statistical
measures of goodness-of-fit between the estimated bed change and analytical approximation.
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Chapter : IAHR World Congress Proceedings
Category : 36th Congress - The Hague (2015) ALL CONTENT
Article : Sediment management and morphodynamics
Date Published : 28/08/2015
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