OJMH  Vol.3 No.1 , January 2013
Streamflow Decomposition Based Integrated ANN Model
Abstract: The prediction of riverflows requires the understanding of rainfall-runoff process which is highly nonlinear, dynamic and complex in nature. In this research streamflow decomposition based integrated ANN (SD-ANN) model is developed to improve the efficacy rather than using a single ANN model for the flow hydrograph. The streamflows are decomposed into two states namely 1) the rise state and 2) the fall state. The rainfall-runoff data obtained from the Kolar River basin is used to test the efficacy of the proposed model when compared to feed-forward ANN model (FF-ANN). The results obtained in this study indicate that the proposed SD-ANN model outperforms the single ANN model in terms of both the statistical indices and the prediction of high flows.
Cite this paper: N. Bhatia, L. Sharma, S. Srivastava, N. Katyal and R. Srivastav, "Streamflow Decomposition Based Integrated ANN Model," Open Journal of Modern Hydrology, Vol. 3 No. 1, 2013, pp. 15-19. doi: 10.4236/ojmh.2013.31003.

[1]   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, No. 1-4, 2000, pp. 138-153. doi:10.1016/S0022-1694(00)00228-6

[2]   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, pp. 1325-1330. doi:10.1002/hyp.554

[3]   A. Jain and S. Srinivasulu, “Integrated Approach to Model Decomposed Flow Hydrograph Using Artificial Neural Network and Conceptual Techniques,” Journal of Hydrology, Vol. 317, No. 3-4, 2005, pp. 291-306.

[4]   J. G. Arnold, P. M. Allen, R. Muttiah and G. Bernhardt, “Automated Base Flow Separation and Recession Analysis Techniques,” Ground Water, Vol. 33, No. 6, 1995, pp. 1010-1018. doi:10.1111/j.1745-6584.1995.tb00046.x

[5]   M. E. Spongberg, “Spectral Analysis of Base Flow Separation with Digital Filters,” Water Resources Research, Vol. 36, No. 3, 2000, pp. 745-752. doi:10.1029/1999WR900303

[6]   L. C. Smith, D. L. Turcotte and B. L. Isacks, “Streamflow Characterization and Feature Detection Using a Discrete Wavelet Transform,” Hydrological Processes, Vol. 12, No. 2, 1998, pp. 233-249. doi:10.1002/(SICI)1099-1085(199802)12:2<233::AID-HYP573>3.0.CO;2-3

[7]   D. Labat, R. Ababou and A. Mangin, “Rainfall-Runoff Relations for Karstic Springs. Part II: Continuous Wavelet and Discrete Orthogonal Multiresolution,” Journal of Hydrology, Vol. 238, No. 3-4, 2000, pp. 149-178. doi:10.1016/S0022-1694(00)00322-X

[8]   S. Y. Liu, X. Z. Quan and Y. C. Zhang, “Application of Wavelet Transform in Runoff Sequence Analysis,” Progress in Nature Science, Vol. 13, No. 7, 2003, pp. 546- 549.

[9]   F. Anctil and D. G. Tape, “An Exploration of Artificial Neural Network Rainfall-Runoff Forecasting Combined with Wavelet Decomposition,” Journal of Environmental Engineering and Science, Vol. 3, No. 1, 2004, pp. S121-S128. doi:10.1139/s03-071

[10]   B. Zhang and S. Govindaraju, “Prediction of Watershed Runoff Using Bayesian Concepts and Modular Neural Networks,” Water Resources Research, Vol. 36, No. 3, 2000, pp. 753-762. doi:10.1029/1999WR900264

[11]   D. Furundzic, “Application Example of Neural Networks for Time Series Analysis: Rainfall Runoff Modeling,” Signal Processing, Vol. 64, No. 3, 1998, pp. 383-396. doi:10.1016/S0165-1684(97)00203-X

[12]   R. J. Abrahart and L. See, “Comparing Neural Network and Autoregressive Moving Average Techniques for the Provision of Continuous River Flow Forecasts in Two Contrasting Catchments,” Hydrological Processes, Vol. 14, No. 11-12, 2000, pp. 2157-2172. doi:10.1002/1099-1085(20000815/30)14:11/12<2157::AID-HYP57>3.0.CO;2-S

[13]   K. L. Hsu, H. V. Gupta, X. Gao, S. Sorooshian and B. Imam, “Self-Organizing Linear Output Map (SOLO): An Artificial Neural Network Suitable for Hydrologic Modeling and Analysis,” Water Resources Research, Vol. 38, No. 12, 2002, pp. 1-17. doi:10.1029/2001WR000795

[14]   K. Hsu, V. H. Gupta and S. Sorooshian, “Artificial Neural Network Modeling of the Rainfall-Runoff Process,” Water Resources Research, Vol. 31, No. 10, 1995, pp. 2517-2530. doi:10.1029/95WR01955

[15]   N. Sajikumar and B. S. Thandaveswara, “A Nonlinear Rainfall-Runoff Model Using an Artificial Neural Network,” Journal of Hydrology, Vol. 216, No. 1-2, 1999, pp. 32-55.

[16]   K. P. Sudheer, P. C. Nayak and K. S. Ramasastri, “Improving Peak Flow Estimates in Artificial Neural Network River Flow Models,” Hydrological Processes, Vol. 17, No. 3, 2003, pp. 677-686. doi:10.1002/hyp.5103

[17]   M. J. Zurada, “An Introduction to Artificial Neural Systems,” West Publishing Company, St Paul, 1997.

[18]   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.

[19]   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.

[20]   K. C. Luk, J. E. Ball and A. Sharma, “A Study of Optimal Model Lag and Spatial Inputs to Artificial Neural Network for Rainfall Forecasting,” Journal of Hydrology, Vol. 227, No. 1-4, 2000, pp. 56-65. doi:10.1016/S0022-1694(99)00165-1

[21]   D. Silverman and J. A. Dracup, “Artificial Neural Networks and Long-Range Precipitation Prediction in California,” Journal of Climate and Applied Meteorology, Vol. 39, No. 1, 2000, pp. 57-66. doi:10.1175/1520-0450(2000)039<0057:ANNALR>2.0.CO;2

[22]   H. R. Maier and G. C. Dandy, “Neural Networks for the Prediction and Forecasting of Water Resources Variables: A Review of Modeling Issues and Applications,” Environmental Modelling & Software, Vol. 15, No. 1, 2000, pp. 101-124. doi:10.1016/S1364-8152(99)00007-9

[23]   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. 43, No. 10, 2007, Article ID: W10407. doi:10.1029/2006WR005352

[24]   J. E. Nash and J. V. Sutcliffe, “River Flow Forecasting through Conceptual Models: 1. A Discussion of Principles,” Journal of Hydrology, Vol. 10, No. 3, 1970, pp. 282-290. doi:10.1016/0022-1694(70)90255-6

[25]   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, No. 4, 2005, Article ID: W04004. doi:10.1029/2004WR003562

[26]   D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning Representations by Back-Propagating Errors,” Nature, Vol. 323, No. 6088, 1986, pp. 533-536. doi:10.1038/323533a0

[27]   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

[28]   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