Prediction of suspended sediment distribution of Karoon River using artificial neural network
Accurate estimation of sediment concentrations in hydraulic sediment transport from different viewpoint such as sediment discharge estimation of river, selection of hydraulic structures and etc. are important. With respect to importance of this issue in this study for prediction of sediment concentration of Karun river multi-layer perceptron artificial neural network (ANN / MLP) was used. For this purpose 125 field data including bottom concentration, flow velocity, nearest distance from the beach, and the total depth of flow and flow depth was used. Three statistical metrics namely mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R^2 ) were used to evaluate the performance of ANN model. The result shows that MLP model with one hidden layer, Sigmoid transfer function and 5 neurons have best structure in the modeling of sediment concentration of Kroon River. The R^2 and RMSE value is equal to 0.953 and 63.37 mg/l in training stage and 0.752 and 203.02 mg/l in testing stage, respectively. Finally, the sensitive analysis also showed that the nearest distance from the beach and flow depth had the most and the least effect on the sediment concentration, respectively.
| Main Authors: | , |
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| Format: | Journal Contribution biblioteca |
| Language: | Persian |
| Published: |
2018
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| Subjects: | Modeling, Sediment concentration, Artificial neural networks, Distribution, Sediment, |
| Online Access: | http://hdl.handle.net/1834/14756 |
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| Summary: | Accurate estimation of sediment concentrations in hydraulic sediment transport from different
viewpoint such as sediment discharge estimation of river, selection of hydraulic structures and
etc. are important. With respect to importance of this issue in this study for prediction of
sediment concentration of Karun river multi-layer perceptron artificial neural network (ANN /
MLP) was used. For this purpose 125 field data including bottom concentration, flow
velocity, nearest distance from the beach, and the total depth of flow and flow depth was used.
Three statistical metrics namely mean absolute error (MAE), root mean square error (RMSE)
and coefficient of determination (R^2
) were used to evaluate the performance of ANN model.
The result shows that MLP model with one hidden layer, Sigmoid transfer function and 5
neurons have best structure in the modeling of sediment concentration of Kroon River. The R^2
and RMSE value is equal to 0.953 and 63.37 mg/l in training stage and 0.752 and 203.02 mg/l
in testing stage, respectively. Finally, the sensitive analysis also showed that the nearest
distance from the beach and flow depth had the most and the least effect on the sediment
concentration, respectively. |
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