Comparison of Artificial Neural Network Models for Sediment Yield Prediction at Single Gauging Station of Watershed in Eastern India
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 1
Abstract
This paper describes the application of two different neural network models, the standard-back propagation (SBP) model and the radial basis neural network (RBNN) model, to predict monthly sediment yield as a function of monthly rainfall and runoff during the rainy season for a watershed area in India. Four scenarios were considered to determine the type and number of inputs for the artificial neural network (ANN) model. It was observed that in the small and forested watershed of Nagwa, the inclusion of monthly precipitation and average discharge values improved the performance of the ANN model in the estimation of monthly sediment yield. The momentum rate, number of nodes at the hidden layer, number of nodes at the prototype layer, linear coefficient, learning rule, and transfer functions were optimized based on lowest root-mean-square error and highest correlation coefficient values. The optimized parameters were used for the SBP and RBNN models. During validation periods, the RBNN model was closer to the observed values than SBP. The mean annual observed sediment yield was . The mean annual simulated sediment yields were found to be 3.1 and in SBP during training and validation periods. RBNN simulated mean annual sediment yields of 3.6 and during training and validation periods. The results are indicative that the RBNN model is more appropriate for forecasting/simulating the sediment yield at a single point of interest in agricultural watersheds.
Get full access to this article
View all available purchase options and get full access to this article.
References
Agarwal, A., Rai, R. K., and Upadhyay, A. (2009). “Forecasting of runoff and sediment yield using artificial neural networks.” J. Water Res. Protect., 1(05), 368–375.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology. I: Preliminary concepts.” J. Hydrol. Eng., 5(2), 115–123.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology. II: Hydrologic applications.” J. Hydrol. Eng., 5(2), 124–137.
Cigizoglu, H. K., and Kisi, O. (2006). “Methods to improve the neural network performance in suspended sediment estimation.” J. Hydrol., 317(3–4), 221–238.
Dawson, C. W., and Wilby, R. L. (1998). “An artificial neural network approach to rainfall-runoff modeling.” Hydrol. Sci. J., 43(1), 47–65.
Dawson, C. W., and Wilby, R. L. (2001). “Hydrological modelling using artificial neural networks.” Prog. Phys. Geog., 25(1), 80–108.
De Farias, C. A. S., Alves, F. M., Santos, C. A. G., and Koichi, S. (2010). “An ANN-based approach to modelling sediment yield: a case study in a semi-arid area of Brazil.” Proc., ICCE Symp.: Sediment Dynamics for a Changing Future, International Commission on Continental Erosion, Rennes, France, 337, 316–321.
DTREG Predictive Modeling Software [Computer software]. (2009). Phillip H. Sherrod, 〈www.dtreg.com〉 (May 31, 2009).
El-swaify, S. A., Dangler, E. W., and Armstrong, C. L. (1982). Soil erosion by water in tropics, Univ. of Hawaii, College of Agriculture, Research Extension Series 24, Honolulu, 15–48.
Giustolisi, O., and Laucelli, D. (2005). “Improving generalization of artificial neural networks in rainfall-runoff modeling.” Hydrol. Sci. J., 50(3), 439–457.
Hsu, K. L., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 2517–2530.
Jain, A., and Srinivasulu, S. (2006). “Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques.” J. Hydrol., 317(3–4), 291–306.
Kaur, R., Srinivasan, R., Mishra, K., Dutta, D., Prasad, D., and Bansal, G. (2003). “Assessment of a SWAT model for soil and water management in India.” Land Use Water Resour. Res., 3, 1–7.
Lorrai, M., and Sechi, G. M. (1995). “Neural nets for modelling rainfall-runoff transformations.” Water Res. Manage., 9(4), 299–313.
Maier, H. R., and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications.” Environ. Modell. Softw., 15(1), 101–124.
Minns, A. W., and Hall, M. J. (1996). “Artificial neural networks as rainfall runoff models.” Hydrol. Sci. J., 41(3), 399–417.
Mutlu, E., Chaubey, I., Hexmoor, H., and Bajwa, S. G. (2008). “Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed.” Hydrol. Processes, 22(26), 50–97.
Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models: Part I. A discussion of principles.” J. Hydrol., 10(3), 282–290.
NeuralWorks Professional II/Plus [Computer software]. NeuralWare, Pittsburgh.
Raghuwanshi, N. S., Singh, R., and Reddy, L. S. (2006). “Runoff and sediment yield modeling using artificial neural networks: Upper Siwane River, India.” J. Hydrol. Eng., 11(1), 71–79.
Rajurkar, M. P., Kothyari, U. C., and Chaube, U. C. (2002). “Artificial neural networks for daily rainfall-runoff modelling.” Hydrol. Sci. J., 47(6), 865–877.
Sarangi, A., and Bhattacharya, A. K. (2005). “Comparison of artificial neural network and regression models for sediment loss prediction from Banha watershed in India.” Agric. Water Manage., 78(3), 195–208.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall runoff modeling using artificial neural networks.” J. Hydrol. Eng., 4(3), 232–239.
Wang, Y. M., Kerh, T., and Traore, S. (2009). “Neural networks approaches for modelling river suspended sediment concentration due to tropical storms.” Global NEST J., 11(4), 457–466.
Wilby, R. L., Abrahart, R. J., and Dawson, C. W. (2003). “Detection of conceptual model rainfall-runoff processes inside an artificial neural network.” Hydrol. Sci. J., 48(2), 163–181.
Yenigün, K., Mahmut, B., Reşit, G., and Mehmet, M. (2010). “A comparative study on prediction of sediment yield in the Euphrates Basin.” Int. J. Phys. Sci., 5(5), 518–534.
Information & Authors
Information
Published In
Copyright
© 2013 American Society of Civil Engineers.
History
Received: May 13, 2010
Accepted: Feb 3, 2012
Published online: Feb 18, 2012
Published in print: Jan 1, 2013
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.