Rainfall Runoff Modelling Using Generalized Neural Network and Radial Basis Network

Abstract

Rainfall runoff study has a wide scope in water resource management. To provide a reliable prediction model is of paramount importance. Runoff prediction is carried out using generalized regression neural network and radial basis neural network. Daily Rainfall runoff model was developed for Nethravathi river basin located at the west coast of Karnataka, India. The comparative study showed Radial basis neural network performed better than generalized neural network during its evaluation by performance indicators

Keywords

• Generalized regression neural network, Radial basis neural network, Runoff Modelling

References

1. D. Achela, K. Fernando, and A. W. Jayawardena, “Runoff forecasting using RBF networks with OLS algorithm,” Journal of hydrologic engineering, vol.3, pp.203-209. July 1998.
2. Ajai Singh, Mohd. Imtiyaz, R. K. Isaac, and D. M. Denis, “ Assessing the performance and uncertainty analysis of the SWAT and RBNN models for simulation of sediment yield in the Nagwa watershed, India,” hydrological sciences Journal, vol.59, pp.351-364. August 2014.
3. L. E. Besaw, D. M. Rizzo, P. R. Bierman, and W. R. Hackett, “Advances in ungauged streamflow prediction using artificial neural networks,” Journal of Hydrology, vol.386, pp.27–37, February 2010.
4. C. Dawson, and R. Wilby, “An Artificial Neural Network Approach to Rainfall – Runoff Modelling,” Hydrological Sciences Journal, vol. 43, pp.47-66, Febraury1998.
5. Hikmet karem cigizoglu, “Application of Generalized Regression Neural Networks to Intermittent Flow Forecasting and Estimation,” Journal of hydrologic engineering, vol.10, pp.336-341, August 2005.
6. Hilmi Kerem Cigizoglu, and Murat Alp, “Rainfall-Runoff Modelling Using Three Neural Network Methods,” International Conference on Artificial Intelligence and Soft Computing, 3070, pp166-171. 2004
7. A. W. Minns, and M.J. Hall, “Artificial neural networks as rainfall runoff models,” Hydrological Sciences Journal, vol.41, pp.399-417, June 1996.
8. D. K. Pratihar, Soft Computing, 2nd Ed., New Delhi: Narosa Publishing House Pvt. Ltd. 2008.

9. M. P. Rajurkar, U. C. Kothyari, and U. C. Chaube, “Modelling of Daily Rainfall runoff relationship with artificial neural networks,” Journal of Hydrology, vol.285, pp.96-113, August 2003.
10. A. Sezin Tokar, and A. J. Peggy, “Rainfall-Runoff Modelling Using Artificial Neural Network,” Journal of Hydrologic Engineering, vol.4, pp.232-239, July 1999.
11. D. F. Specht, “A General Regression Neural Network,” IEEE Transactions on Neural Networks, vol.2, pp.568-576, November1991.
12. Tahir Erdem Ozturk, “Artificial Neural Networks Approach for Earthquake Deformation Determination of Geosynthetic Reinforced Retaining Walls,” International Journal of Intelligent Systems and Applications in Engineering, vol.2, pp.1-9, 2014