Prediction of Density Flow Plunging Depth in Dam Reservoirs: An Artificial Neural Network Approach

Experimental findings and observations indicate that plunging flow is related to the formation of bed load deposition in dam reservoirs. The sediment delta begins to form in the plunging region where the inflow river water meets the ambient reservoir water. Correct estimation of dam reservoir flow, plunging point, and plunging depth is crucial for dam reservoir sedimentation and water quality issues. In this study, artificial neural network (ANN), multi-linear regression (MLR), and two-dimensional hydrodynamic model approaches are used for modeling the plunging point and depth. A multi layer perceptron (MLP) is used as the ANN structure. A two-dimensional model is adapted to simulate density plunging flow through a reservoir with a sloping bottom. In the model, nonlinear and unsteady continuity, momentum, energy, and k–e turbulence equations are formulated in the Cartesian coordinates. Density flow parameters such as velocity, plunging points, and plunging depths are determined from the simulation and model results, and these are compared with previous experimental and model works. The results show that the ANN model forecasts are much closer to the experimental data than the MLR and mathematical model forecasts.

[1]  C. R. Shah,et al.  Plunging phenomenon of density currents in reservoirs , 1971 .

[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]  Roey Egozi,et al.  Experiments on the effect of hydrograph characteristics on vertical grain sorting in gravel bed rivers , 2006 .

[4]  H. Stefan,et al.  Negatively buoyant flow in a diverging channel. II: 3-D flow field descriptions , 1987 .

[5]  G. Parker,et al.  Note on the analysis of plunging of density flows , 2007 .

[6]  G. Parker,et al.  Progradational sand-mud deltas in lakes and reservoirs. Part 1. Theory and numerical modeling , 2003 .

[7]  U. C. Kothyari,et al.  Modeling of the daily rainfall-runoff relationship with artificial neural network , 2004 .

[8]  H. K. Cigizoglu,et al.  Incorporation of ARMA models into flow forecasting by artificial neural networks , 2003 .

[9]  D. Spalding,et al.  A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows , 1972 .

[10]  F. Üneş.  Analysis of plunging phenomenon in dam reservoirs using three-dimensional density flow simulations , 2008 .

[11]  F. Üneş.  Investigation of density flow in dam reservoirs using a three-dimensional mathematical model including Coriolis effect , 2008 .

[12]  H. Stefan.  HIGH CONCENTRATION TURBIDITY CURRENTS IN RESERVOIRS , 1973 .

[13]  Anton Schleiss,et al.  Circulation in Stratified Lakes due to Flood-Induced Turbidity Currents , 2006 .

[14]  Dimitri P. Solomatine,et al.  On the encapsulation of numerical-hydraulic models in artificial neural network , 1999 .

[15]  J. Imran,et al.  Three-Dimensional Modeling of Negatively Buoyant Flow in Diverging Channels , 2003 .

[16]  Hikmet Kerem Cigizoglu,et al.  Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods , 2008 .

[17]  Hong‐Yuan Lee,et al.  Experimental Study of Reservoir Turbidity Current , 1997 .

[18]  K. P. Sudheer,et al.  Explaining the internal behaviour of artificial neural network river flow models , 2004 .

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

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

[21]  S. Savage,et al.  Analysis Of Plunging Phenomena In Water Reservoirs , 1975 .

[22]  Marcelo Horacio Garcia,et al.  Depositional Turbidity Currents Laden with Poorly Sorted Sediment , 1994 .

[23]  P. B. Deolalikar,et al.  Estimation of scour below spillways using neural networks , 2006 .

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

[25]  H. Stefan,et al.  Negatively buoyant flow in a diverging channel. I: Flow regimes , 1987 .

[26]  M. Arita,et al.  Plunging conditions of two-dimensional negative buoyant surface jets released on a sloping bottom , 2008 .

[27]  H. Stefan,et al.  Buoyancy Induced Plunging Flow Into Reservoirs and Costal Regions , 1986 .

[28]  Mathematical modeling of plunging reservoir flows , 1988 .

[29]  Eduardo A. Varas,et al.  Artificial neural networks for streamflow prediction , 2002 .

[30]  H. Maier,et al.  The Use of Artificial Neural Networks for the Prediction of Water Quality Parameters , 1996 .

[31]  Yonas B. Dibike,et al.  Application of artificial neural networks to the simulation of a two dimensional flow , 1999 .

[32]  H. Stefan,et al.  Plunging Flow into a Reservoir: Theory , 1984 .

[33]  Hong‐Yuan Lee,et al.  Experiments on deposition behavior of fine sediment in a reservoir , 2000 .

[34]  Marcelo Horacio Garcia,et al.  Experiments on turbidity currents over an erodible bed , 1987 .

[35]  J. Imberger,et al.  Collie River Underflow into the Wellington Reservoir , 1979 .

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

[37]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .