Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques / Modélisation de la charge journalière en matières en suspension dans des rivières turques à l'aide de plusieurs techniques empiriques

Abstract The transport of sediment load in rivers is important with respect to pollution, channel navigability, reservoir filling, longevity of hydroelectric equipment, fish habitat, river aesthetics and scientific interest. However, conventional sediment rating curves cannot estimate sediment load accurately. An adaptive neuro-fuzzy technique is investigated for its ability to improve the accuracy of the streamflow—suspended sediment rating curve for daily suspended sediment estimation. The daily streamflow and suspended sediment data for four stations in the Black Sea region of Turkey are used as case studies. A comparison is made between the estimates provided by the neuro-fuzzy model and those of the following models: radial basis neural network (RBNN), feed-forward neural network (FFNN), generalized regression neural network (GRNN), multi-linear regression (MLR) and sediment rating curve (SRC). Comparison of results reveals that the neuro-fuzzy model, in general, gives better estimates than the other techniques. Among the neural network techniques, the RBNN is found to perform better than the FFNN and GRNN.

[1]  T.,et al.  Training Feedforward Networks with the Marquardt Algorithm , 2004 .

[2]  Özgür Kişi,et al.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt , 2004 .

[3]  A. Turgeon,et al.  Fuzzy Learning Decomposition for the Scheduling of Hydroelectric Power Systems , 1996 .

[4]  Bellie Sivakumar,et al.  River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches , 2002 .

[5]  Narendra Singh Raghuwanshi,et al.  Runoff and Sediment Yield Modeling using Artificial Neural Networks: Upper Siwane River, India , 2006 .

[6]  Ozgur Kisi,et al.  Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones , 2005 .

[7]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[8]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[9]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[10]  Kurt Hornik,et al.  FEED FORWARD NETWORKS ARE UNIVERSAL APPROXIMATORS , 1989 .

[11]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[12]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[13]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[14]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[15]  D. K. Srivastava,et al.  Application of ANN for Reservoir Inflow Prediction and Operation , 1999 .

[16]  D. Solomatine,et al.  Model trees as an alternative to neural networks in rainfall—runoff modelling , 2003 .

[17]  G. Tayfur.  Artificial neural networks for sheet sediment transport , 2002 .

[18]  H. Raman,et al.  Multivariate modelling of water resources time series using artificial neural networks , 1995 .

[19]  H. K. Cigizoglu,et al.  ESTIMATION AND FORECASTING OF DAILY SUSPENDED SEDIMENT DATA BY MULTI-LAYER PERCEPTRONS , 2004 .

[20]  Edward A. McBean,et al.  Uncertainty in Suspended Sediment Transport Curves , 1988 .

[21]  R. Abrahart,et al.  Detection of conceptual model rainfall—runoff processes inside an artificial neural network , 2003 .

[22]  Sharad K. Jain,et al.  Development of Integrated Sediment Rating Curves Using ANNs , 2001 .

[23]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[24]  Hikmet Kerem Cigizoglu,et al.  Estimation, forecasting and extrapolation of river flows by artificial neural networks , 2003 .

[25]  Richard J. Heggen,et al.  Neural Networks for River Flow Prediction , 1995 .

[26]  John E. Freund,et al.  Probability and statistics for engineers , 1965 .

[27]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[28]  Ozgur Kisi,et al.  Methods to improve the neural network performance in suspended sediment estimation , 2006 .

[29]  Slobodan P. Simonovic,et al.  Short term streamflow forecasting using artificial neural networks , 1998 .

[30]  K. Lam,et al.  River flow time series prediction with a range-dependent neural network , 2001 .

[31]  Tarek Sayed,et al.  Comparison of Adaptive Network Based Fuzzy Inference Systems and B-spline Neuro-Fuzzy Mode Choice Models , 2003 .

[32]  Christian W. Dawson,et al.  An artificial neural network approach to rainfall-runoff modelling , 1998 .

[33]  O. Ks.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation , 2004 .

[34]  Soon Heung Chang,et al.  Radial basis function networks applied to DNBR calculation in digital core protection systems , 2003 .