scholarly journals Experimental investigation on inversion of ADVP measurement for suspended sediment concentration, a case study

Author(s):  
Ying Zeng ◽  
Xiaoling Yin ◽  
Chen Lu ◽  
Shuqin Huang
1999 ◽  
Vol 3 (2) ◽  
pp. 285-294 ◽  
Author(s):  
R. Lidén

Abstract. A semi-distributed conceptual model, HBV-SED, for estimation of total suspended sediment concentration and yield at the outlet of a catchment was developed and tested through a case study. The base of the suspended sediment model is a dynamic hydrological model, which produces daily series of areal runoff and rainfall for each sub-basin as input to the sediment routine. A lumped measure of available sediment is accumulated continuously based on a linear relationship between log-transformed values of rainfall and erosion, while discharge of suspended sediment at the sub-basin outlet is dependent on runoff and amount of stored available sediment. Four model parameter are empirically determined through calibration against observed records of suspended sediment concentration. The model was applied to a 200 km2 catchment with high altitude differences in the tropical parts of Bolivia, where recorded suspended sediment concentrations were available during a two-year period. 10,000 parameter sets were generated through a Monte Carlo procedure to evaluate the parameter sensitivity and interdependence. The predictability of the model was assessed through dividing the data record into a calibration and an independent period for which the model was validated and compared to the sediment rating curve technique. The results showed that the slope coefficients of the log-transformed model equations for accumulation and release were much stronger than the intercept coefficients. Despite and existing interdependence between the model parameters, the HBV-SED model gave clearly better results than the sediment rating curve technique for the validation period, indication that the supply-based approached has a promising future as a tool for basic engineering applications.


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.


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