کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6409502 1332870 2016 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Research papersArtificial neural network and regression models for flow velocity at sediment incipient deposition
ترجمه فارسی عنوان
شبکه های عصبی مصنوعی و مدل های رگرسیون برای سرعت جریان در رسوب اولیه رسوب
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
چکیده انگلیسی


- Experimental data of sediment incipient deposition were used in modelling.
- Artificial neural networks and regression models were developed.
- Models were established using flow, fluid, sediment and channel characteristics.
- Feed forward back propagation artificial neural network model was found superior.

A set of experiments for the determination of flow characteristics at sediment incipient deposition has been carried out in a trapezoidal cross-section channel. Using experimental data, a regression model is developed for computing velocity of flow in a trapezoidal cross-section channel at the incipient deposition condition and is presented together with already available regression models of rectangular, circular, and U-shape channels. A generalized regression model is also provided by combining the available data of any cross-section. For comparison of the models, a powerful tool, the artificial neural network (ANN) is used for modelling incipient deposition of sediment in rigid boundary channels. Three different ANN techniques, namely, the feed-forward back propagation (FFBP), generalized regression (GR), and radial basis function (RBF), are applied using six input variables; flow discharge, flow depth, channel bed slope, hydraulic radius, relative specific mass of sediment and median size of sediment particles; all taken from laboratory experiments. Hydrodynamic forces acting on sediment particles in the flow are considered in the regression models indirectly for deriving particle Froude number and relative particle size, both being dimensionless. The accuracy of the models is studied by the root mean square error (RMSE), the mean absolute percentage error (MAPE), the discrepancy ratio (Dr) and the concordance coefficient (CC). Evaluation of the models finds ANN models superior and some regression models with an acceptable performance. Therefore, it is concluded that appropriately constructed ANN and regression models can be developed and used for the rigid boundary channel design.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Hydrology - Volume 541, Part B, October 2016, Pages 1420-1429
نویسندگان
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