OUED EL ABIOD BASIN (ALGERIA): SOLID TRANSPORT ESTIMATION BY THREE ARTIFICIAL NEURAL NETWORK METHODS

A. BOUGAMOUZA, B. REMINI, A. AMMARI, F. SAKHRAOUI

Abstract


The assessment of sediment transport in river is important in water resource management such as the design and control dams and other hydraulic structures. In this paper, Three Artificial Neural Network methods are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the river of Oued El Abiod watershed, Biskra, Algeria. The Feed-forward Neural Networks (FFNN), generalized regression neural networks (GRNN) and the radial basis neural networks (RBNN) models are established for estimating current suspended sediment values. The two criteria RMSE and R2 were used to evaluate the performance of applied models. The comparison of three models showed that the RBNN method provided generally the better than the other methods in estimation of suspended sediment. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision.

Keywords


Artificial Neural Network, Oued Abiod watershed, Generalized Regression, Suspended Sediment.

Full Text:

PDF

References


AIK L.E., ZAINUDDIN Z. (2008). An Improved Fast Training Algorithm for RBF Networks Using Symmetry-Based Fuzzy C-Means Clustering. MATEMATIKA, Vol. 24, No 2, pp. 141–148.

AYDIN A., EKER R. (2012). Prediction of daily suspended sediment load using radial basis function neural networks, Ormancılık Dergisi, Vol. 8, No 2, pp. 36-44.

BISHOP C. M. (1995) Neural networks for pattern recognition, Oxford University Press, Oxford

BROOMHEAD D., LOWE D (1988). Multivariable functional interpolation and adaptive networks. Complex Systems, No 2, pp. 321–355.

CHUTACHINDAKATE C., SUMI T. (2008). Sediment yield and transportation analysis: case study on Managawa river basin, Japan, Annual Journal of Hydraulic Engineering, JSCE, Vol.52, pp. 157-162.

CIGIZOGLU H.K., KISI O. (2006) Methods to improve the neural network performance in suspended sediment estimation, Journal of Hydrology, Vol. 317, Issues 3-4; pp. 221-238.

CIGIZOGLU H. K., KISI O. (2004) Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data, Nordic Hydrology Vol. 36, No 1.

CYBENCO G. (1989) Approximation by superposition of a sigmoidal function. Mathematics of control, Signals and Systems, No 2, pp. 303–314.

EISAZADEH L., SOKOUTI R., HOMAEE M., PAZIRA E. (2013) Modeling sediment yield using artificial neural network and multiple linear regression methods, International Journal of Biosciences, Vol.3, No 9, pp.116- 122

HAGAN M.T., MENHAJ M.B. (1994). Training feed forward networks with the Marquardt algorithm. IEEE Transactions Neural Networks, No 6, pp. 861–867.

HAYKIN S. (1999) Neural networks: a comprehensive foundation, Prentice-Hall, Upper Saddle River, NJ

HU T. S., LAM K. C., NG S. T. (2001) River flow time series prediction with a range dependent neural network, Hydrological Sciences Journal, Vol. 46, No 5, pp. 729-745.

JAYAWARDENA A.W., XU P.C., TSANG F.L., LI W.K. (2006). Determining the structure of a radial basis function network for prediction of nonlinear hydrological time series, , Hydrological Sciences Journal Vol. 51, No 1, pp. 21-44.

KISI O., YUKSEL I., DOGAN E. (2008). Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques, Hydrological Sciences, Vol. 53, No 6, pp. 1270-1285.

LEE G. C., CHANG S. H. (2003). Radial basis function networks applied to DNBR calculation in digital core protection systems, Annals of Nuclear Energy, Vol.30, pp.1561–1572

NOURANI V., MOGADDAM A.A., NADIRI A.O. (2008) An ANN based model forecasting, Hydrological Processes, Vol. 22, pp. 5054-5066

PAVELSKY T.M., SMITH L.C. (2009). Remote sensing of suspended sediment concentration, flow velocity, and Lake Recharge in the Peace-Athabasca Delta, Canada, Water Resources Research, Vol. 45, Issue 11, pp. 1-16.

REMINI B., HALLOUCHE W. (2007). Studying Sediment, International Water Power and Dam construction, pp. 42-45.

SINGH V. P., KRSTANOVIC P. F. (1987) A stochastic model for sediment yield using principle of maximum entropy. Water Resources Research, Vol. 23, pp. 781-793.

SPECHT D.F. (1991) A general regression neural network, IEEE Transactions Neural Networks, Vol. 2, No 6, pp. 568–576.

WANG Y. M., KERH T., TRAORE S. (2009) Neural Networks Approaches for modeling river suspended sediment concentration due to tropical storms, Global Nest Journal, No 11, pp. 457-466.

WASSERMAN P. D (1993) Advanced Methods in Neural Computing, pp. 155–161. Van Nostrand Reinhold, New York, USA

WILBY R. L., ABRABART R. J., DAWSON C. W. (2003) Detection of conceptual model rainfall-runoff processes inside an artificial neural network, Hydrological Sciences Journal, Vol. 48, No 2, pp. 163-181.

ZHU Y. M., LU X. X., ZHOU Y. (2007) Suspended sediment flux modeling with artificial neural network: An example of the Long chuanjiang River in the Upper Yangtze Catchment, China, Science Direct, Geomorphology, No 84, pp. 111-125


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.