Evaluating suspended sediment and turbidity reduction projects in a glacially‐conditioned catchment through dynamic regression and fluvial process‐based modeling

2021 ◽  
Author(s):  
Kezhen Wang ◽  
W. Dany Davis ◽  
Scott Steinschneider
Keyword(s):  
Suspended Sediment ◽  
Fluvial Process

Observed Suspended Sediment Profiles under the Effect of Single and Double Peaked Wave Spectra

2006 ◽  
Author(s):  
M. R. Delgado Blanco ◽  
M. Olabarrieta Lizaso ◽  
A. Giardino ◽  
R. Banasiak ◽  
R. Verhoeven ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Wave Spectra ◽  
Sediment Profiles

THE INITIAL RESULTS OF THE SUSPENDED SEDIMENT DYNAMICS DURING THE DRY SEASON IN THE HAU RIVER MOUTH AREA

2017 ◽  
Vol 17 (2) ◽  
Author(s):  
Nguyen Ngoc Tien ◽  
Dinh Van Uu ◽  
Nguyen Tho Sao ◽  
Do Huy Cuong ◽  
Nguyen Trung Thanh ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
River Mouth ◽  
Dry Season ◽  
Sediment Dynamics ◽  
Mouth Area ◽  
River Mouth Area ◽  
Initial Results

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

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