Modelling suspended sediment concentration using artificial neural networks for Gangotri glacier

2015 ◽  
Vol 30 (9) ◽  
pp. 1354-1366 ◽  
Author(s):  
Rajesh Joshi ◽  
Kireet Kumar ◽  
Vijay Pal Singh Adhikari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Gangotri Glacier ◽  
Artificial Neural

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.

scholarly journals Prediction and classification of suspended sediment and zooplankton signals from acoustic Doppler current profiler backscatter data using artificial neural networks

2021 ◽  
Vol 944 (1) ◽  
pp. 012014
Author(s):  
A Dwinovantyo ◽  
S Solikin ◽  
H M Manik ◽  
T Prartono ◽  
Susilohadi
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Acoustic Doppler Current Profiler ◽  
Suspended Sediments ◽  
Sediment Concentration ◽  
Optimal Number ◽  
Environmental Data ◽  
Zooplankton Abundance ◽  
Current Profiler ◽  
Artificial Neural

Abstract Characterization of each underwater object has its challenges, especially for small objects. The process of quantifying acoustic signals for these small objects can be done using high-frequency hydroacoustic instruments such as an acoustic Doppler current profiler (ADCP) combined with the artificial intelligence (AI) technique. This paper presents an artificial neural network (ANN) methodology for classifying an object from acoustic and environmental data in the water column. In particular, the methodology was tuned for the recognition of suspended sediments and zooplankton. Suspended sediment concentration and zooplankton abundance, which extracted from ADCP acoustic data, were used as input in the backpropagation method along with other environmental data such as effects of tides, currents, and vertical velocity. The classifier used an optimal number of neurons in the hidden layer and a feature selection based on a genetic algorithm. The ANN method was also used to estimate the suspended sediment concentration in the future. This study provided new implications for predicting and classifying suspended sediment and zooplankton using the ADCP instrument. The proposed methodology allowed us to identify the objects with an accuracy of more than 95%.

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