P. MITTAL ET AL.

1028

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 Fran-

cisco, 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 Sim-

plified Approach to Quantifying Predictive and Paramet-

ric 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,” Wa-

ter 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 Sci-

ence, Vol. 41, No. 1, 1996, pp. 399-417.

[9] C. W. Dawson and R. Wilby, “An Artificial Neural Net-

work Approach to Rainfall Runoff Modelling,” Hydro-

logical 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 Re-

sources Research, Vol. 35, No. 4, 1999, pp. 1191-1197.

doi:10.1029/1998WR900086

[11] C. E. Imrie, S. Durucan and A. Korre, “River Flow Pre-

diction 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 Hydro-

logic 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 Hy-

drologic 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 Neu-

rofuzzy 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 De-

termination for Neural Network Models in Water Re-

sources 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 de-

termination for Neural Network Models in Water Re-

sources 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 Proc-

esses, 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,” Envi-

ronmental 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

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