Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/4627
Title: Artificial neural networks for sheet sediment transport
Other Titles: Application des réseaux de neurones artificiels pour le transport sédimentaire en nappe
Authors: Tayfur, Gökmen
Tayfur, Gökmen
Izmir Institute of Technology. Civil Engineering
Keywords: Artificial neural networks
Sediment transport
Transport capacity
Sheet sediments
Issue Date: Dec-2002
Publisher: Taylor and Francis Ltd.
Source: Tayfur, G. (2002). Artificial neural networks for sheet sediment transport. Hydrological Sciences Journal, 47(6), 879-892. doi:10.1080/02626660209492997
Abstract: Sheet sediment transport was modelled by artificial neural networks (ANNs). A three-layer feed-forward artificial neural network structure was constructed and a back-propagation algorithm was used for the training of ANNs. Event-based, runoff-driven experimental sediment data were used for the training and testing of the ANNs. In training, data on slope and rainfall intensity were fed into the network as inputs and data on sediment discharge were used as target outputs. The performance of the ANNs was tested against that of the most commonly used physically-based models, whose transport capacity was based on one of the dominant variables-flow velocity (V), shear stress (SS), stream power (SP), and unit stream power (USP). The comparison results revealed that the ANNs performed as well as the physically-based models for simulating nonsteady-state sediment loads from different slopes. The performances of the ANNs and the physically-based models were also quantitatively investigated to estimate mean sediment discharges from experimental runs. The investigation results indicated that better estimations were obtained for V over mild and steep slopes, under low rainfall intensity; for USP over mild and steep slopes, under high rainfall intensity; for SP and SS over very steep slopes, under high rainfall intensity; and for ANNs over steep and very steep slopes, under very high rainfall intensities.
URI: http://doi.org/10.1080/02626660209492997
http://hdl.handle.net/11147/4627
ISSN: 0262-6667
0262-6667
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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