Neural network prediction of bed material load transport

@article{Kumar2012NeuralNP,
  title={Neural network prediction of bed material load transport},
  author={Bimlesh Kumar},
  journal={Hydrological Sciences Journal},
  year={2012},
  volume={57},
  pages={956 - 966}
}
  • B. Kumar
  • Published 1 July 2012
  • Engineering
  • Hydrological Sciences Journal
Abstract Bed material load, which comprises bed load and suspended load, has been extensively studied in the past few decades and many equations have been developed, but they differ from each other in derivation and form. If a process can be related to various flow conditions on a general basis, a proper understanding of bed material load movement can be ascertained. As the process is extremely complex, obtaining a deterministic or analytical form of it is too difficult. Neural network… 
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21 Citations
Background Citations
6
Methods Citations
4

21 Citations

Prediction of bed load sediments using different artificial neural network models
Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide…
Prediction of bedload transport rate using a block combined network structure
TLDR
It is shown that BCNN as a new method with an appropriate accuracy level could effectively be adopted for bedload prediction purposes and showed superior performance accuracy compared to other ANNs, and to empirical models.
Updating the neural network sediment load models using different sensitivity analysis methods: a regional application
TLDR
Two optimum predictive models subjected to artificial neural network (ANN) were developed and it was demonstrated that the ranked parameters using SA due to covering more uncertainties can be more reliable.
Bedload transport predictions based on field measurement data by combination of artificial neural network and genetic programming
TLDR
In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia and the ability of the artificial neural network combined with genetic programming with R2 equal to 0.95, RMSE equal to0.1 as a precipitation predictive tool for predicting the bed load transport rate was observed as being acceptable.
The application of backpropagation neural network method to estimate the sediment loads
Nearly all formulations of conventional sediment load estimation method were developed based on a review of laboratory data or data field. This approach is generally limited by local so it is only…
Prediction of sediment transport rates in gravel-bed rivers using Gaussian process regression
Estimating sediment transport rate in rivers has high importance due to the difficulties and costs associated with its measurement, which has drawn the attention of experts in water engineering. In…
Determination of influential parameters for prediction of total sediment loads in mountain rivers using kernel-based approaches
It is important to have a reasonable estimation of sediment transport rate with respect to its significant role in the planning and management of water resources projects. The complicate nature of…
Regression model for sediment transport problems using multi-gene symbolic genetic programming
Bedload transport and temporal variation of non-uniform sediment in a seepage-affected alluvial channel
ABSTRACT Understanding bedload transport fluctuations in rivers is crucial for complementing the existing knowledge on sediment transport theory. In this contribution, we use a natural-scale…
A comprehensive study on the application of firefly algorithm in prediction of energy dissipation on block ramps
TLDR
The results indicate that the ANFIS-FA is the most stable integrative model in comparison to the other embedded methods and reveal that GMDH and SVR are the moststable technique among all applied models.
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References

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    Proceedings of the National Academy of Sciences
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TLDR
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