Estimating sand concentrations using ADCP‐based acoustic inversion in a large fluvial system characterized by bi‐modal suspended‐sediment distributions

2019 ◽  
Vol 44 (6) ◽  
pp. 1295-1308 ◽  
Author(s):  
Ricardo N. Szupiany ◽  
Cecilia Lopez Weibel ◽  
Massimo Guerrero ◽  
Francisco Latosinski ◽  
Molly Wood ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Fluvial System ◽  
Acoustic Inversion

Acoustic Inversion Method for Profiling Suspended Sediment Concentration

2014 ◽  
Vol 687-691 ◽  
pp. 3980-3983
Author(s):  
Jun Xi Shi ◽  
Min Zhu ◽  
Yan Bo Wu ◽  
Xing Tao Sun
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Acoustic Backscatter ◽  
Inversion Method ◽  
Dual Frequency ◽  
Frequency Method ◽  
Backscatter Signal ◽  
Size Profile ◽  
Acoustic Inversion

The concentration of suspended sediment is an important parameter for the research of sediment transport. Acoustic backscatter technique has been employed to measure the concentration of suspended sediment recently. It is an inversion problem to measure the concentration from the backscatter signal. In this paper, an improved dual-frequency method is proposed for the concentration inversion of suspension sediment. It is an explicit solution with much lower computational complexity than the commonly used iterative method and with no requirement of known and constant particle size profile compared to the basic dual-frequency method.

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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