Development of Integrated Sediment Rating Curves Using ANNs

Correct estimation of sediment volume being carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. Artificial neural networks (ANNs) are a simplied mathematical representation of the functioning of the human brain. Three-layer feed-forward ANNs have been shown to be a powerful tool for input-output mapping and have been widely used in water resources problems. The ANN approach is used to establish an integrated stage-discharge-sediment concentration relation for two sites on the Mississippi River. Based on the comparison of the results for two gauging sites, it is shown that the ANN results are much closer to the observed values than the conventional technique.

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

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

[3]  N. Null.  Artificial Neural Networks in Hydrology. I: Preliminary Concepts , 2000 .

[4]  K. Thirumalaiah,et al.  River Stage Forecasting Using Artificial Neural Networks , 1998 .

[5]  A. W. Minns,et al.  Artificial neural networks as rainfall-runoff models , 1996 .

[6]  Leung Tsang,et al.  A Neural Network Approach to Inversion of Snow Water Equivalent from Passive Microwave Measurements , 1992 .

[7]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .

[8]  Sharad K. Jain,et al.  Setting Up Stage-Discharge Relations Using ANN , 2000 .

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

[10]  Witold F. Krajewski,et al.  Rainfall forecasting in space and time using a neural network , 1992 .

[11]  R. Ferguson.  River Loads Underestimated by Rating Curves , 1986 .

[12]  Richard C. Peralta,et al.  Optimal design of aquifer cleanup systems under uncertainty using a neural network and a genetic algorithm , 1999 .

[13]  B. S. Thandaveswara,et al.  A non-linear rainfall–runoff model using an artificial neural network , 1999 .

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

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

[16]  John F. Kennedy,et al.  Menu of Coupled Velocity and Sediment‐Discharge Relations for Rivers , 1990 .

[17]  V. Chandramouli,et al.  Deriving a General Operating Policy for Reservoirs Using Neural Network , 1996 .

[18]  M. Jansson.  Estimating a sediment rating curve of the Reventazón river at Palomo using logged mean loads within discharge classes , 1996 .

[19]  Peter F. Ffolliott,et al.  SEDIMENT RATING CURVES FOR A CLEARCUT PONDEROSA PINE WATERSHED IN NORTHERN ARIZONA1 , 1993 .

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

[21]  Razi Gaskari,et al.  Performance of a Virtual Runoff Hydrograph System , 1996 .

[22]  Kuolin Hsu,et al.  Artificial Neural Network Modeling of the Rainfall‐Runoff Process , 1995 .