Share This Article:

Dual Artificial Neural Network for Rainfall-Runoff Forecasting

Abstract Full-Text HTML Download Download as PDF (Size:253KB) PP. 1024-1028
DOI: 10.4236/jwarp.2012.412118    11,716 Downloads   30,590 Views   Citations

ABSTRACT

One of the principal issues related to hydrologic models for prediction of runoff is the estimation of extreme values (floods). It is well understood that unless the models capture the dynamics of rainfall-runoff process, the improvement in prediction of such extremes is far from reality. In this paper, it is proposed to develop a dual (combined and paralleled) artificial neural network (D-ANN), which aims to improve the models performance, especially in terms of extreme values. The performance of the proposed dual-ANN model is compared with that of feed forward ANN (FF-ANN) model, the later being the most common ANN model used in hydrologic literature. The forecasting exercise is carried out for hourly river flow data of Kolar Basin, India. The results of the comparison indicate that the D-ANN model performs better than the FF-ANN model.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

P. Mittal, S. Chowdhury, S. Roy, N. Bhatia and R. Srivastav, "Dual Artificial Neural Network for Rainfall-Runoff Forecasting," Journal of Water Resource and Protection, Vol. 4 No. 12, 2012, pp. 1024-1028. doi: 10.4236/jwarp.2012.412118.

References

[1] K. J. Beven, “Rainfall-Runoff Modelling: The Primer,” John Wiley, Hoboken, 2000.
[2] Q. Duan, S. Sorooshian and V. K. Gupta, “Effective and Efficient Global Optimization for Conceptual Rainfall Runoff Models,” Water Resources Research, Vol. 28, No. 4, 1992, pp. 1015-1031.
[3] G. E. P. Box and G. M. Jenkins, “Time Series Analysis: Forecasting and Control,” Holden Day Inc., San Francisco, 1976.
[4] A. Jain, and S. Srinivasulu, “Development of Effective and Efficient Rainfall-Runoff Models Using Integration of Deterministic, Real-Coded Genetic Algorithms and Artificial Neural Network Techniques,” Water Resources Research, Vol. 40, , No. 4, 2004, Article ID: W04302. doi:10.1029/2003WR002355
[5] R. K. Srivastav, K. P. Sudheer and I. Chaubey, “A Simplified Approach to Quantifying Predictive and Parametric Uncertainty in Artificial Neural Network Hydrologic Models,” Water Resources Research, Vol. 31, No. 10, 2007, pp. 2517-2530.
[6] K. Hsu, V. H. Gupta and S. Sorooshian, “Artificial Neural Network Modelling of the Rainfall-Runoff Process,” Water Resources Research, Vol. 31, No. 10, 1995, pp. 2517-2530. doi:10.1029/95WR01955
[7] K. Hornik, M. Stichcombe and H. White, “Multi Layer Feed forward Networks Are Universal Approximators,” Neural Networks, Vol. 2, 1989, pp. 359-366.
[8] A. W. Minns and M. J. Hall, “Artificial Neural Networks as Rainfall-Runoff Models,” Journal of Hydrology Science, Vol. 41, No. 1, 1996, pp. 399-417.
[9] C. W. Dawson and R. Wilby, “An Artificial Neural Network Approach to Rainfall Runoff Modelling,” Hydrological Science, Vol. 43, No. 1, 1998, pp. 47-66.
[10] M. Campolo, P. Andreussi and A. Soldati, “River Flood Forecasting with a Neural Network Model,” Water Resources Research, Vol. 35, No. 4, 1999, pp. 1191-1197. doi:10.1029/1998WR900086
[11] C. E. Imrie, S. Durucan and A. Korre, “River Flow Prediction Using Artificial Neural Networks: Generalization beyond the Calibration Range,” Journal of Hydrology, Vol. 233, 2000, pp. 138-153. doi:10.1016/S0022-1694(00)00228-6
[12] N. Karunanithi, W. J. Grenney, D. Whitley and K. Bovee, “Neural Networks for River Flow Prediction,” Journal of Computing in Civil Engineering, Vol. 8, No. 2, 1994, pp. 201-220. doi:10.1061/(ASCE)0887-3801(1994)8:2(201)
[13] ASCE Task Committee, “Artificial Neural Networks in Hydrology-I: Preliminary Concepts,” Journal of Hydrologic Engineering, Vol. 5, No. 2, 2000, pp. 115-123. doi:10.1061/(ASCE)1084-0699(2000)5:2(115)
[14] ASCE Task Committee, “Artificial Neural Networks in Hydrology-II: Hydrologic Applications,” Journal of Hydrologic Engineering, Vol. 5, No. 2, 2000, pp. 124-137. doi:10.1061/(ASCE)1084-0699(2000)5:2(124)
[15] P. C. Nayak, K. P. Sudheer, D. M. Rangan and K. S. Ramasastri, “Short-Term Flood Forecasting with a Neurofuzzy Model,” Water Resources Research, Vol. 41, 2005, Article ID: W04004. doi:10.1029/2004WR003562
[16] G. J. Bowden, G. C. Dandy and H. R. Maier, “Input Determination for Neural Network Models in Water Resources Applications: 1. Background and Methodology,” Journal of Hydrology, Vol. 301, No. 1-4, 2004, pp. 75-92. doi:10.1016/j.jhydrol.2004.06.021
[17] G. J. Bowden, G. C. Dandy and H. R. Maier, “Input determination for Neural Network Models in Water Resources Applications: 2. Background and Methodology,” Journal of Hydrology, Vol. 301, No. 1-4, 2004, pp. 93- 107. doi:10.1016/j.jhydrol.2004.06.020
[18] K. P. Sudheer, A. K. Gosain and K. S. Ramasastri, “A Data-Driven Algorithm for Constructing Artificial Neural Network Rainfall-Runoff Models,” Hydrological Processes, Vol. 16, No. 6, 2002, 1325-1330. doi:10.1002/hyp.554
[19] H. R. Maier and G. C. Dandy, “Neural Networks for the Prediction and Forecasting of Water Resources Variables: A Review of Modelling Issues and Applications,” Environmental Modelling & Software, Vol. 15, No. 1, 2000, pp. 101-124. doi:10.1016/S1364-8152(99)00007-9
[20] A. Y. Shamseldin, “Application of a Neural Network Technique to Rainfall-Runoff Modelling,” Journal of Hydrology, Vol. 199, No. 3-4, 1997, pp. 272-294. doi:10.1016/S0022-1694(96)03330-6

  
comments powered by Disqus

Copyright © 2019 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.