Suspended sediment concentration distribution using Tsallis entropy

2014 ◽  
Vol 414 ◽  
pp. 31-42 ◽  
Author(s):  
Vijay P. Singh ◽  
Huijuan Cui
Keyword(s):  
Suspended Sediment ◽  
Concentration Distribution ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Tsallis Entropy

scholarly journals Suspended sediment concentration distribution in an alluvial channel flow

1994 ◽  
Vol 16 (1) ◽  
pp. 15-19
Author(s):  
Dang Huu Chung
Keyword(s):  
Mathematical Model ◽  
Suspended Sediment ◽  
Channel Flow ◽  
Concentration Distribution ◽  
Input Data ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Concentration Gradients ◽  
Alluvial Channel ◽  
The Finite Difference Method

In this paper a numerical solution for the problem on suspended sediment concentration distribution in an alluvial channel flow has been computed on the base of PROFILE model proposed by L. C. Van Rijn for the case of uniform flow. Although, in the present case, mathematical model is quite simple, but it is exact enough to apply to Somme problems in practice. The aim of the paper is that the author would like to use the finite difference method for the same problem. The input data was used from experiment flume. The result showed that the concentration distribution fast decreased along the channel and concentration gradients became very small at the sections situated far enough from the upstream. Besides, a computing program me with the ability of graphic expression was established. 

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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