JWARP> Vol.1 No.5, November 2009

Forecasting of Runoff and Sediment Yield Using Artificial Neural Networks

DownloadDownload as PDF (Size:413KB)  HTML    PP. 368-375  

ABSTRACT

Runoff and sediment yield from an Indian watershed during the monsoon period were forecasted for differ-ent time periods (daily and weekly) using the back propagation artificial neural network (BPANN) modeling technique. The results were compared with those of single- and multi-input linear transfer function models. In BPANN, the maximum value of variable was considered for normalization of input, and a pattern learning algorithm was developed. Input variables in the model were obtained by comparing the response with their respective standard error. The network parsimony was achieved by pruning the network using error sensitiv-ity - weight criterion, and model generalization by cross validation. The performance was evaluated using correlation coefficient (CC), coefficient of efficiency (CE), and root mean square error (RMSE). The single input linear transfer function (SI-LTF) runoff and sediment yield forecasting models were more efficacious than the multi input linear transfer function (MI-LTF) and ANN models.

Cite this paper

A. AGARWAL, R. RAI and A. UPADHYAY, "Forecasting of Runoff and Sediment Yield Using Artificial Neural Networks," Journal of Water Resource and Protection, Vol. 1 No. 5, 2009, pp. 368-375. doi: 10.4236/jwarp.2009.15044.

References

[1] A. S. Tokar and M. Markus, “Precipitation-runoff model-ling using artificial neural networks and conceptual mod-els,” Journal of Hydrologic Engineering, Vol. 5, No. 2, pp. 156?161, 2000.
[2] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” Parallel Distributed Processing, MIT Press, Cambridge, Vol. I, pp. 318?362, 1986.
[3] D. E. Rumelhart, B. Widrow, and M. A. Letr, “The basic ideas in neural networks” Communications of the ACM, Vol. 37, No. 3, pp. 87?92, 1994.
[4] A. Y. Shamseldin, K. M. O'Connor, and G. C. Liang, “Methods for combining the outputs of different rain-fall-runoff models,” Journal of Hydrology, Vol. 197, pp. 203?229, 1997.
[5] “ASCE (2000a) Task Committee on application of artifi-cial neural networks in hydrology,” Artificial Neural Networks in Hydrology, I: Preliminary Concepts, Journal of Hydrologic Engineering, ASCE, Vol. 5, No. 2, pp. 124?137.
[6] “ASCE (2000b) Task Committee on application of artifi-cial neural networks in hydrology” Artificial Neural Networks in Hydrology, II: Hydrologic Application, Journal of Hydrologic Engineering, ASCE, Vol. 5, No. 2, pp. 115?123.
[7] A. Jagadeesh, B. Zhang, and R. S. Govindaraju, “Com-parison of ANNs and empirical approaches for predicting watershed runoff,” Journal of Water Resources Planning and Management, Vol. 126, No. 3, pp. 156?166, 2000.
[8] A. S. Tokar and M. Markus, “Precipitation-runoff model-ling using artificial neural networks and conceptual mod-els,” Journal of Hydrologic Engineering, Vol. 5, No. 2, pp. 156?161, 2000.
[9] H. M. Nagy, K. Watanabe, and M. Hirano, “Prediction of sediment load concentration in rivers using artificial neu-ral network model,” J. Hydraulic Engrg., Vol. 128, No. 6, pp. 588?595, 2002.
[10] M. P. Rajurkar, U. C. Kothyari, and U. C. Chaube, “Modeling of the daily rainfall-runoff relationship with artificial neural networks,” Journal of Hydrology, Vol. 285, pp. 96?113, 2004.
[11] K. P. Sudheer and S. K. Jain, “Radial basis function neu-ral network for modeling rating curves.” J. Hydrologic Engrg., Vol. 8, No. 3, pp. 161?164, 2003.
[12] T. W. Kim and J. B. Valdes, “Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks,” J. Hydrologic Engrg., Vol. 8, No. 6, pp. 319?328, 2003.
[13] H. Moradkhani, K. L. Hsu, H. V. Gupta, and S. So-rooshian, “Improved streamflow forecasting using self- organizing radial basis function artificial neural net-works,” Journal of Hydrology, Vol. 295, pp. 246?262, 2004.
[14] J. Olsson, C. B. Uvo, K. Jinno, A. Kawamura, K. Nishi-yama, N. Koreeda, T. Nakashima, and O. Morita, “Neural networks for rainfall forecasting by atmospheric down-scaling.” J. Hydrologic Engrg., Vol. 9, No. 1, pp. 1?12, 2004.
[15] Keskin, M. Erol, and T. ?zlem, “Artificial neural net-work models of daily pan evaporation” Journal of Hy-drologic Engrg., Vol. 11, No. 1, pp. 65?70. 2006.
[16] N. S. Raghuwanshi, R. Singh, and L. S. Reddy, “Runoff and sediment yield modeling using artificial neural net-works: Upper siwane river,” J. Hydrologic Engrg., India, Vol. 11, No. 1, pp. 71?79, 2006.
[17] J. Johnston, Econometric Methods, McGraw Hill, Tokyo, 1972.
[18] H. Akaike, “A new look at the statistical model identifi-cation,” IEEE Transactions on Automatic Control, Vol. AC-19, pp. 716?723, 1974.
[19] Rissanenj, “Modeling by short data description,” Auto-mation, Vol. 14, pp. 465?471, 1978.
[20] E. D. Karnin, “A simple procedure for pruning back propagation trained neural networks,” Institute of Electri- cal and Electronics Engineers Transactions on Neural Networks, Vol. 1, No. 20, pp. 239?242, 1990.
[21] Z. X. Xu and J. Y. Lijy, “Short term inflow forecasting using an artificial neural network model,” Hydrological Process, Vol. 16, pp. 2423?2439, 2002.
[22] P. S. Yu, C. L. Liu, and T. Y. Lee, “Application of a transfer function model to a storage runoff process,” Sto-chastic and Statistical Methods in Hydrology and Envi-ronmental Engineering, Vol. 3, pp. 87?97, 1994.
[23] K. W. Hipel, A. Ian, U. S. Panum, and V. P. Singh, “Sto-chastic and statistical methods in hydrology and envi-ronmental engineering,” The series analysis in hydrology and environmental engineering, Kluwer Academic Pub-lishers, The Netherlands, Vol. 3, 1994.

comments powered by Disqus

Copyright © 2014 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.