Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7702
Title: Artificial neural networks for estimating daily total suspended sediment in natural streams
Authors: Tayfur, Gökmen
Güldal, Veysel
Tayfur, Gökmen
Izmir Institute of Technology. Civil Engineering
Keywords: Artificial neural networks
Back-propagation
Parameter range
Sediment graph theory
Suspended sediment
Issue Date: 2006
Publisher: IWA Publishing
Source: Tayfur, Gökmen, and Güldal, V. (2006). Artificial neural networks for estimating daily total suspended sediment in natural streams. Nordic Hydrology, 37(1), 69-79. doi:10.2166/nh.2006.0006
Abstract: Estimates of sediment loads in natural streams are required for a wide spectrum of water resources engineering problems from optimal reservoir design to water quality in lakes. Suspended sediment constitutes 75-95% of the total load. The nonlinear problem of suspended sediment estimation requires a nonlinear model. An artificial neural network (ANN) model has been developed to predict daily total suspended sediment (TSS) in rivers. The model is constructed as a three-layer feedforward network using the back-propagation algorithm as a training tool. The model predicts TSS rates using precipitation (P) data as input. For network training and testing 240 sets of data sets were used. The model successfully predicted daily TSS loads using the present and past 4 days precipitation data in the input vector with R2 = 0.91 and MAE = 34.22 mg/L. The performance of the model was also tested against the most recently developed non-linear black box model based upon two-dimensional unit sediment graph theory (2D-USGT). The comparison of results revealed that the ANN has a significantly better performance than the 2D-USGT. Investigation results revealed that the ANN model requires a period of more than 75 d of measured P-TSS data for training the model for satisfactory TSS estimation. The statistical parameter range (xmin - xmax) plays a major role for optimal partitioning of data into training and testing sets. Both sets should have comparable values for the range parameter.
URI: https://doi.org/10.2166/nh.2006.0006
https://hdl.handle.net/11147/7702
ISSN: 2224-7955
2224-7955
0029-177
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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