Wavelet Analysis on Suspended Sediment Concentration Variations over a Tidal Cycle in the Lianyungang Nearshore Area, China

2011 ◽  
Vol 356-360 ◽  
pp. 857-861
Author(s):  
Cun Yong Zhang
Keyword(s):  
Wavelet Analysis ◽  
Suspended Sediment ◽  
Current Velocity ◽  
Suspended Sediment Concentration ◽  
Lower Layer ◽  
Periodic Oscillation ◽  
Sediment Concentration ◽  
Nearshore Area ◽  
Scale Characteristics ◽  
Calm Weather

To investigate the characteristics of the suspended sediment concentration (SSC) temporal variation, one representative SSC profile and its relationship with current velocity profile in calm weather in the Lianyungang nearshore area were analyzed using wavelet analysis. Results indicate that the variations of the SSC had multi-time scale characteristics and the fluctuations varied in different time periods. The correlation of periodic oscillation in the upper layers between SSC and current velocity was much more significant than the corresponding lower layer correlation. For the scale more than 4 hours, the curves of SSC and current turned out to be out phase, while this phenomenon was not obvious for the scale less than 4 hours. Wavelet analysis provided further insights into the characteristics of the suspended sediment concentration.

scholarly journals Simulation Model Pattern Distribution Sediment at Ambon Bay, Indonesia

2018 ◽  
Vol 203 ◽  
pp. 01009 ◽  
Author(s):  
Mukhsan Putra Hatta ◽  
Muhammad Arsyad Thaha ◽  
Marthen P Lakatua
Keyword(s):  
Distribution Pattern ◽  
Suspended Sediment ◽  
Current Velocity ◽  
Suspended Sediment Concentration ◽  
Research Result ◽  
Movement Pattern ◽  
Sediment Concentration ◽  
Sediment Distribution ◽  
Velocity Effect ◽  
Current Movement

The sedimentation occurring in the outer Ambon bay as a result of the effluent from Way Ruhu river causes the reduction the mouth of the outer Ambon Bay, so that it influences the current movement pattern and sediment distribution. This research aimed at analyzing the current movement pattern and sediment distribution pattern in the outer Ambon bay using two scenarios: with the reclamation and without reclamation. The research used the numerical model of MIKE 21DHI with modules of the hydrodynamic and mud transport. The research result indicates that the simulation current velocity with the absence of reclamation ranged from 0.0-0.4 m/s, while with the presence of the reclamation, the current velocity is slightly higher that is between 0.0 - 0.6 m/s the various velocity patterns. The sediment distribution pattern in the outer Ambon bay as the result of the effluent from the way Ruhu river follows the current movement. If the conditions leading to the tide and the highest tide, the sediment going out from way Ruhu river is pushed into the bay and vice versa, when the condition leading to the recede and lowest recede, the sediment concentration is pulled out from the bay. The suspended sediment concentration is between 0.002 kg/m3 - 0.030 kg/m3 with the various patterns. The suspended sediment concentration to the condition without reclamation is more collecting if compared with the suspended sediment concentration by the presence of the distributing reclamation because of current velocity effect.

scholarly journals LAGRANGIAN DROGUE-BASED DRIFTER FOR MONITORING SUSPENDED SEDIMENT TRANSPORT IN INTERTIDAL ENVIRONMENTS

2011 ◽  
Vol 1 (32) ◽  
pp. 81
Author(s):  
Takahiro Nishi ◽  
Charles Lemckert ◽  
Kentaro Hayashi ◽  
Fumihiko Yamada
Keyword(s):  
Suspended Sediment ◽  
Current Velocity ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Spatial Distributions ◽  
Flow Conditions ◽  
Current Profile ◽  
Directional Flow ◽  
Temporal And Spatial ◽  
Sediment Settling

The in-situ Lagrangian-Acoustic Drogue (LAD) presented by Schacht and Lemckert (2007) for monitoring the temporal and spatial distributions of both the current and the suspended sediment concentration within the estuary environments has been modified to operate in the shallow water intertidal regions. The new drogue, called the LAD for Inter-Tidal environments (LAD-IT), is equipped with a Global Positioning System (GPS), a small Acoustic Doppler Current Profiler (ADCP) and nephelometer. The small ADCP, which did not have a bottom tracking facility, was used to maximize the range of depths the LAD-IT could operate over. The accuracy of a vertical current profile measured using the LAD-IT was examined through the laboratory experiments conducted at an outdoor stream pool in Kumamoto, Japan, with uni-directional flow conditions, and through the field experiments conducted within an intertidal zone of Ariake Sound in Kumamot, Japan, with multi-directional flow conditions. Under uni- directional flow conditions the current profile was measured within 7% accuracy by summing the surface current velocity calculated using GPS tracking and the relative current profile measured using ADCP. Under multi-directional flow conditions, such as those of tide and wind-induced wave fields, the current profile agreed within 5% accuracy. This was partly because both Eulerian and Lagrangian mass transport velocities under these conditions were on the order of 1cm/s, and thus the error value was very small. The temporal and spatial distributions of both the current velocity and the suspended sediment concentration were also measured using the LAD-IT on the Brisbane River, Australia under uni-directional flow conditions. The field observation results support the conventional concept of the suspended sediment as a vertical balance between downward suspended sediment settling and upward turbulent diffusion fluxes. The results indicate the LAD-IT is adequate for estimating the sediment settling velocity in the field.

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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