Neural Networks and Rainfall-Runoff Model, its Calibration and Validation

U. Ghani, A.R. Ghumman, M.A. Shamim

Abstract

In this study a rainfall-runoff model was developed with the help of neural networks. Input to the model is precipitation and potential evapotranspiration (both on monthly basis). Output from the model is the simulated runoff at the watershed outlet. The model was calibrated and tested for Brandu river catchment of Pakistan.The data was collected from Meteorological Department Pakistan. Statistical results showed that the model preformed well. The correlation co-efficient between the simulated and measured data was found to be 87.5%.

References

  1. Abulohom, M. S. 1997. Calibration of a mathematical model for generating monthly River Flows from meteorological data for a selected catchment. M.Sc thesis, CEWRE, UET, Lahore, Pakistan.
  2. Abulohom, M. S&.S. M .S. Shah, A.R.Ghumman 2001. Development of a Rainfall-Runoff Model, its Calibration and Validation. Journal of Water Resources Management. Vol. 15 No.3. 2001
  3. Alley, W.M. 1984. On the treatment of evapotranspiration, soil moisture accounting an aquifer recharge in monthly water balance models. Water resources Research Vol. 20(8): 1137-1149.
  4. C,-Y.XU & Vandewiele, G.L. 1995. Sensitivity of monthly rainfall-runoff models to input errors and data length. Hydrological Sciences Journal. Vol. 39(2): 157-176.
  5. Dawson, C. W. and Wilby, R. L.2001. “ Hydrological Modeling using Artificial Neural Networks”, Progress in Physical Geography, Arnold.
  6. French, M.N.; Krajewski, W.F. and Cuykendall, R.R.1992. “Rainfall forecasting in space and time using a neural network”, Journal of Hydrology, Vol. 137, 1-31.
  7. Hughes, D.A. 1995. Monthly rainfall-runoff models applied to arid and semiarid catchments for water resources estimation purposes. Hydrological Sciences Journal. Vol. 40(6): 751- 769.
  8. Liden, R. & Harlin. J. 2000. Analysis of conceptual rainfall-runoff modeling performance in different climates. Journal of Hydrology Vol. 238: 231-247.
  9. Madsen, H. 2000. Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. Journal of Hydrology Vol. 235: 276-288.
  10. Mather, J.R 1981 Using computed streamflow in watershed analysis. Water resources bulletin vole 17(3): 474-482
  11. Mutreja, K.M. 1986. Hydrologic synthesis and simulation. Applied Hydrology. Pp. 613-668.
  12. Nash, J.E. and Sutcliffe, J.V.1970, “River flow forecasting through conceptual models, part I, A discussion of Principles”, Journal of Hydrology, 10(3), pp. 282-290.
  13. Oscar R.Dolling and Eduardo A. Varas. 2002. Artificial Neural Networks For Streamflow.Prediction. Journal of Hydraulic Research Vol 40,2002,No.5.
  14. Pitman, W.V. 1973. A mathematical model for generating monthly River Flows from meteorological data in South Africa. A Ph.D. thesis, Hydrological Research Unit, University of Witwatersrand, South Africa.
  15. Soil survey of Pakistan department, 1975. Reconnaissance soil survey. Soil survey of Pakistan.
  16. Stefan Uhlenbrook, Jan Scibert, Christian Leibundgut and Allan Rodhe. 1999. Prediction uncertainty of conceptual rainfall-runoff models caused by problems in identifying model parameters and structure. Hydrological Sciences Journal Vol 44(5): 779-797.
  17. Tawatchai Tangsanchali. Forecasting Model of Chao Phraya River Flood Levels at Bangkok.
  18. Vandewiele, G.L, and Atlabachew, E. 1995. Monthly water balance of ungauged catchments obtained by geographical regionalization. Journal of Hydrology, Vol. 170: 277- 291.
  19. Vandewiele, G.L, Chong-Yu Xu, and Ni-Lar-Win. 1992. Methodology and comparative study of monthly water balance models in Belgium, China and Burma. Journal of Hydrology, Vol. 134: 315-347.
  20. Vandewiele, G.L. and Ni-Lar-Win 1998. Monthly water balance models for 55 basins in 10 Countries. Hydrological Sciences Journal. Vol. 43(5): 687-699.
  21. Victor Miguel Ponce. 1989. Ctachment Routing. Engineering Hydrology. Pp.306-331.
  22. Yi-Ming Kuo, Chen-Wuing Liu, Kao-Hung Lin. Evaluation of the ability of an Artificial Neural network model to assess the variation of Groundwater quality in an area of blackfoot disease in Taiwan. Water Research 38(2004) 148-158.
  23. Yu Pao-Shan & Chang-Yan Tao. 2000. FUZZY multi objective function for rainfall-runoff model calibration. Journal of Hydrology Vol 238: 1-14.
  24. Zhu, M.L. and Fujita, M.1994, “Comparison between fuzzy reasoning and neural networks methods to forecast runoff discharge”, Journal of Hydro science and Hydraulic Engineering, Vol. 12, No. 2, 131-141.
Download


Paper Citation


in Harvard Style

Ghani U., Ghumman A. and Shamim M. (2004). Neural Networks and Rainfall-Runoff Model, its Calibration and Validation . In Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development - Volume 1: ANNs, (ICINCO 2004) ISBN 972-8865-14-7, pages 60-66. DOI: 10.5220/0001150000600066


in Bibtex Style

@conference{anns04,
author={U. Ghani and A.R. Ghumman and M.A. Shamim},
title={Neural Networks and Rainfall-Runoff Model, its Calibration and Validation},
booktitle={Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development - Volume 1: ANNs, (ICINCO 2004)},
year={2004},
pages={60-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001150000600066},
isbn={972-8865-14-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Workshop on Artificial Neural Networks: Data Preparation Techniques and Application Development - Volume 1: ANNs, (ICINCO 2004)
TI - Neural Networks and Rainfall-Runoff Model, its Calibration and Validation
SN - 972-8865-14-7
AU - Ghani U.
AU - Ghumman A.
AU - Shamim M.
PY - 2004
SP - 60
EP - 66
DO - 10.5220/0001150000600066