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The dynamics of suspended sediment involves inherent non-linearity and complexity because of existence of both spatial variability of the basin characteristics and temporal climatic patterns. This complexity, therefore, leads to inaccurate prediction by the conventional sediment rating curve (SRC) and other empirical methods. In the present study, feed-forward back propagation (FFBP) algorithm of ANNs is used to model stage–discharge–suspended sediment relationship for ablation season (May–September) for melt runoff released from Gangotri glacier, one of the largest glaciers in Himalaya

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This page is a summary of: Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier, Hydrological Processes, November 2015, Wiley,
DOI: 10.1002/hyp.10723.
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