A model study on the large-scale effect of macrofauna on the suspended sediment concentration in a shallow shelf sea

2018 ◽  
Vol 211 ◽  
pp. 62-76 ◽  
Author(s):  
M.H. Nasermoaddeli ◽  
C. Lemmen ◽  
G. Stigge ◽  
O. Kerimoglu ◽  
H. Burchard ◽  
...  
Keyword(s):  
Suspended Sediment ◽  
Scale Effect ◽  
Large Scale ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Model Study ◽  
Shelf Sea

scholarly journals Automatic calibration of a large-scale sediment model using suspended sediment concentration, water quality, and remote sensing data

RBRH ◽  
2019 ◽  
Vol 24 ◽  
Author(s):  
Hugo de Oliveira Fagundes ◽  
Fernando Mainardi Fan ◽  
Rodrigo Cauduro Dias de Paiva
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Suspended Sediment ◽  
Large Scale ◽  
Suspended Sediment Concentration ◽  
Remote Sensing Data ◽  
Sediment Concentration ◽  
Spatial Discretization ◽  
Calibration And Validation ◽  
Sediment Model

ABSTRACT Calibration and validation are two important steps in the application of sediment models requiring observed data. This study aims to investigate the potential use of suspended sediment concentration (SSC), water quality and remote sensing data to calibrate and validate a large-scale sediment model. Observed data from across 108 stations located in the Doce River basin was used for the period between 1997-2010. Ten calibration and validation experiments using the MOCOM-UA optimization algorithm coupled with the MGB-SED model were carried out, which, over the same period of time, resulted in 37 calibration and 111 validation tests. The experiments were performed by modifying metrics, spatial discretization, observed data and parameters of the MOCOM-UA algorithm. Results generally demonstrated that the values of correlation presented slight variations and were superior in the calibration step. Additionally, increasing spatial discretization or establishing a background concentration for the model allowed for improved results. In a station with high quantity of SSC data, calibration improved the ENS coefficient from -0.44 to 0.44. The experiments showed that the spectral surface reflectance, total suspended solids and turbidity data have the potential to enhance the performance of sediment models.

scholarly journals Estimation of Suspended Sediment Concentration in Downstream of the Ba River Basin using Remote Sensing Images

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Ba Dung NGUYEN ◽  
Tuyet Minh DANG
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
River Basin ◽  
Large Scale ◽  
Suspended Sediment Concentration ◽  
Image Interpretation ◽  
Sediment Concentration ◽  
Initial Result ◽  
Remote Sensing And Gis ◽  
Landsat Satellite

Assessing the tendency of suspended sediment concentration (SSC) in the river watershedsenables a better understanding of the hydromorphological properties of its basins and the associatedprocesses. In addition, analyzing this trend is essential to address several important issues such as erosion,water pollution, human health risks, etc. Therefore, it is critical to determine a proper method to quantifyspatio-temporal variability in SSC. In recent years, remote sensing and GIS technologies are being widelyapplied to support scientists, researchers, and environmental resource investigators to quickly andsynchronously capture information on a large scale. The combination of remote sensing and GIS data willbecome the reliable and timely updated data source for the managers, researchers on many fields. Thereare several tools, software, algorithms being used in extracting information from satellites and support forthe analysis, image interpretation, data collection. The information from satellite images related to waterresources includes vegetational cover, flooding events on a large scale, rain forecast, populationdistribution, forest fire, landslide movements, sedimentation, etc., and especially information on waterquality, sediment concentration. This paper presents the initial result from LANDSAT satellite imageinterpretation to investigate the amount of sediment carried downstream of the Ba river basin.

scholarly journals Suspended Sediment Concentration and Turbulence behind Large-scale Submerged Breakwater

2013 ◽  
Vol 69 (2) ◽  
pp. I_581-I_585
Author(s):  
Akiyoshi KATANO ◽  
Nobuyuki KURUSHIMA ◽  
Sakae NAGAI ◽  
Hiroshi IZUMIDA ◽  
Toshihiro SIMIZU
Keyword(s):  
Suspended Sediment ◽  
Large Scale ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Submerged Breakwater

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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