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Information Journal Paper

Title

Prediction of suspended sediment distribution of Karoon River using artificial neural network

Pages

  27-35

Abstract

 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 (R2) 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 R2 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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    APA: Copy

    BAHRAMI, HOSSEIN, & EMAMGHOLIZADEH, SAMAD. (2018). Prediction of suspended sediment distribution of Karoon River using artificial neural network. JOURNAL OF MARINE SCIENCES AND TECHNOLOGY, 17(2 ), 27-35. SID. https://sid.ir/paper/361575/en

    Vancouver: Copy

    BAHRAMI HOSSEIN, EMAMGHOLIZADEH SAMAD. Prediction of suspended sediment distribution of Karoon River using artificial neural network. JOURNAL OF MARINE SCIENCES AND TECHNOLOGY[Internet]. 2018;17(2 ):27-35. Available from: https://sid.ir/paper/361575/en

    IEEE: Copy

    HOSSEIN BAHRAMI, and SAMAD EMAMGHOLIZADEH, “Prediction of suspended sediment distribution of Karoon River using artificial neural network,” JOURNAL OF MARINE SCIENCES AND TECHNOLOGY, vol. 17, no. 2 , pp. 27–35, 2018, [Online]. Available: https://sid.ir/paper/361575/en

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