International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Sediment Yield Modelling of Kal River in Maharashtra Using Artificial Neural Network Model

Author Affiliations

  • 1SWCE, CTAE, MPUAT, Udaipur, Rajasthan, INDIA
  • 2SWE, CTAE, MPUAT, Udaipur, Rajasthan, INDIA

Res. J. Recent Sci., Volume 4, Issue (ISC-2014), Pages 120-130, (2015)


The sediment yield is important factor concern with erosion rate from the catchment which is caused the problems of reducing the storage capacity of reservoirs, creating delta at mouth of rivers and reduces capacity of streamflow, etc. There are several models developed for estimation of sediment yield like USLE, RUSEL and physical based models like SWAT, but they required rigours series of data. In present study artificial neural network model is non linear Black box model used to forecast the sediment yield of Kal river in Maharashtra using the streamflow, stream flow lag by one or two day, rainfall and sediment yield lag one or two day as input to the model. In present study multi layers feed forward back propagation neural network model with one to three input layers, one hidden layers and one output layers were developed. The models were adopted by changing numbers of neuron in hidden layers and epoch. The models performance was evaluated by statistical indices such as R, RMSE, CE, VE, MAD, and MAPE. The study reveal that, ANN model with single input as streamflow and 10 neuron in hidden layer found R values 0.92 and 0.85 during training and cross validation respectively and other indices such as RMSE, CE, VE, MAD and MAPE were 91.58 tons/day, 84.16 per cent, 2.28 per cent, -4.52 per cent and 98.07 per cent during training period where 110.35 ton/day, 76.82 per cent, 0.1 per cent, 10.62 per cent and 20.91 per cent during cross validation period, respectively. It is also observed that, the performance of model increase with increases input parameter and changing combination inputs parameters. The linear regress model developed to compare the performance, found the ANN model performance were better and overall ANN model performance were satisfactory for prediction of sediment yield.


  1. Boukhrissa Z.A., Khanchaoul K., Bissaonnais Y., Le., Tourki M., Prediction of Sediment Load by Sediment Rating Curve and Artifitial Neural Network in El Kabir Catchment Algeria, J of Earth Syst Sci., 122 (5), 1303-1312, (2013)
  2. Cigizoglu H.K., Suspended Sediment Yield Estimation and Forecasting using Artificial Neural Network, Turkey. J.Eng Environ Sci., 26,15-25, (2002)
  3. Agrawal Avinash, Rai, R.K. and Uppadhya Alka., Forecasting of Runoff and Sediment Yield using Artificial Neural Network, J. of Water Resources and Protection,1, 368-375, (2009)
  4. Karunanithi N., Grenney W.J., Whitely D. and Bovee K., Neural networks for river flow prediction, J. Comp. Civil Eng., ASCE,8, 201-220, (1994)
  5. Tokar A.S. and Markus M., Precipitation-runoff model-ling using artificial neural networks and conceptual models, J of Hydrologic Engineering,5(2), 156 161, (2000)
  6. Rumelhart D.E, Hinton G.E., and Williams R.J., Learning internal representations by error propagation. Parallel Distributed Processing, MIT Press, Cambridge,1, 318 362, (1986)
  7. Rumelhart D.E., Widrow B. and Letr M.A., The basic ideas in neural networks, Communications of the ACM,37(3), 87 92, (1994)
  8. Shamseldin A.Y., O'Connor K.M. and Liang G.C., Methods for combining the outputs of different rain-fall-runoff models, Journal of Hydrology,197, 203 229, (1997)
  9. Kisi O., Suspended sediment estimation using neuro-fuzzy and neural network approaches, Hydrol. Sci. J., 50(4), 683-695, (2005)
  10. Agrawal A., Singh R.D., Mishra S.K. and Bhunya P.K., ANN based sediment yield model for Vamsadhara river basin (India), J. Water, SA.,31(1), 4378-4738, (2005)
  11. Jha S.K. and Jain A., Evaluation of ANN technique for rainfall-runoff modeling in a large watershed. Proceedings of the international conference on Hydrological perspective for sustainable development –HYPESD, IIT Roorkee,180-181, (2005)
  12. Raghuwansi N.S., Singh R. and Reddy L.S., Runoff and Sediment Yield Modeling using Artificial Neural Network: Upper Siwane River, J. Hydrol. Engng ASCE,11(1), 71-79(2006)
  13. Viessman W. and Lewis G.L., Introduction to Hydrology,Prentice-Hall of India Pvt Ltd, New Delhi,(2008)
  14. Flood I. and Kartam N., Neural Network in Civil Engineering I. Principle and Understanding, J of Computing in Civil Engg,.8(2), 131-148, (1994)
  15. Mins A.W. and Hall M.J., Artificial neural network as a rainfall runoff models. Hydrological Science Journal, 41(3), 399-417 (1996)
  16. Stone M.B., Cross valedictory choice and assessment of statistical prediction, J. of the Royal Statistical Society, 36, 111-147, (1974)
  17. Samani N., M. Gohari-Moghadam and A.A. Safavi, A simple neural network model for the determination of aquifer parameters, J. Hydrol., 340, 1–11 (2007)
  18. Chaow V.T.H. and Book of applied Hydrology. McGraw Hills New York, 538, (1964)
  19. Abraham B. and Ledoltor J., Statistical Methods for forecasting. John Wiley and Sons Inc., New York, 472, (1983)
  20. Nash J.F. and Sutcliffe J.V., River flow forecasting through conceptual models, J. hydrol Sci.,44, 399-417, (1970)
  21. Habaied H., Trouch P.A. and De Torch P.P., A coupled rainfall runoff and runoff routing model for adoptive real time forecasting, Water Resources Manag, 5, 47-61, (1991)
  22. Yu. P.S, Liu. C.l. and Lee T.Y., Application of a transfer function model to a storage runoff process, Proc of Stochastic and Statistical methods in hydrology and Environmental Engineering. Beijing, 87-97 (1994)
  23. Kachroo R.K., HOMS Workshop on River Flow Forecasting, Nanjing, China, Unpublished Internal Report, Dept. of Engrg. Hydr., University College Galway, Ireland, (1986)