Turbidimetric Measuring of the Suspended Sediment Concentration in the Coastal Zone

Author(s):  
Ruben D. Kos’yan ◽  
Igor S. Podymov ◽  
Sergey Yu. Kuznetsov
Author(s):  
Рубен Косян ◽  
Ruben Kosyan ◽  
Marina Krylenko ◽  
Marina Krylenko

Results of the suspended sediment concentration study in the coastal zone on the basis of field and laboratory experiments are analyzed. Data from field experiments, performed in the coastal zone of the North, Mediterranean and Black seas, are used. The laboratory researches were fulfilled in the Big Wave Channel of the Hannover University. It is shown that to increase the accuracy of measurement it is necessary to take into account the convective mechanism of the sediment suspension, as well as the size, the direction of the rotation axis and other parameters of the turbulent vortices transporting sand sediments. The presented information will help to improve the quality of field data collection.


Author(s):  
Рубен Косян ◽  
Ruben Kosyan ◽  
Marina Krylenko ◽  
Marina Krylenko

Results of the suspended sediment concentration study in the coastal zone on the basis of field and laboratory experiments are analyzed. Data from field experiments, performed in the coastal zone of the North, Mediterranean and Black seas, are used. The laboratory researches were fulfilled in the Big Wave Channel of the Hannover University. It is shown that to increase the accuracy of measurement it is necessary to take into account the convective mechanism of the sediment suspension, as well as the size, the direction of the rotation axis and other parameters of the turbulent vortices transporting sand sediments. The presented information will help to improve the quality of field data collection.


2013 ◽  
Vol 11 (4) ◽  
pp. 457-466

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.


2021 ◽  
Vol 180 ◽  
pp. 108107
Author(s):  
Guillaume Fromant ◽  
Nicolas Le Dantec ◽  
Yannick Perrot ◽  
France Floc'h ◽  
Anne Lebourges-Dhaussy ◽  
...  

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