MATEC Web Conf.
Volume 101, 2017Sriwijaya International Conference on Engineering, Science and Technology (SICEST 2016)
|Number of page(s)||6|
|Published online||09 March 2017|
The application of backpropagation neural network method to estimate the sediment loads
1 Civil Engineering Departement, Faculty of Civil and Environmental Engineering, ITB, 40132 Bandung, Indonesia
2 Water Resources Development Center, Bandung Institute of Technology, 40132 Bandung, Indonesia
* Corresponding author: firstname.lastname@example.org
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 suitable for a particular river typology. From previous studies, the amount of sediment load tends to be non-linear with respect to the hydraulic parameters and parameter that accompanies sediment. The dominant parameter is turbulence, whereas turbulence flow velocity vector direction of x, y and z. They were affected by water bodies in 3D morphology of the cross section of the vertical and horizontal. This study is conducted to address the non-linear nature of the hydraulic parameter data and sediment parameter against sediment load data by applying the artificial neural network (ANN) method. The method used is the backpropagation neural network (BPNN) schema. This scheme used for projecting the sediment load from the hydraulic parameter data and sediment parameters that used in the conventional estimation of sediment load. The results showed that the BPNN model performs reasonably well on the conventional calculation, indicated by the stability of correlation coefficient (R) and the mean square error (MSE).
© The Authors, published by EDP Sciences, 2017
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