ABSTRACT This paper focuses on a concept of using dimensionless variables as input and output to Artificial Neural Network (ANN) and discusses the improvement in the results in terms of various performance criteria as well as simplification of ANN structure for modeling rainfall-runoff process in certain Indian catchments. In the present work, runoff is taken as the response (output) variable while rainfall, slope, area of catchment and forest cover are taken as input parameters. The data used in this study are taken from six drainage basins in the Indian provinces of Madhya Pradesh, Bihar, Rajasthan, West Bengal and Tamil Nadu, located in the different hydro-climatic zones. A standard statistical performance evaluation measures such as root mean square (RMSE), Nash–Sutcliffe efficiency and Correlation coefficient were employed to evaluate the performances of various models developed. The results obtained in this study indicate that ANN model using dimensionless variables were able to provide a better representation of rainfall–runoff process in comparison with the ANN models using process variables investigated in this study.
Cite this paper
nullM. Goyal and C. Ojha, "Analysis of Mean Monthly Rainfall Runoff Data of Indian Catchments Using Dimensionless Variables by Neural Network," Journal of Environmental Protection, Vol. 1 No. 2, 2010, pp. 155-171. doi: 10.4236/jep.2010.12020.
 M. N. French, W. F. Krajewski and R. R. Cuykendall, “Rainfall Forecasting in Space and Time Using a Neural Network,” Journal of Hydrology, Vol. 137, No. 1-4, Au-gust 1992, pp. 1-31.
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, April 1994, pp. 201-220.
J. C. I. Dooge, “Problems and Methods of Rainfall-Run- off Modeling,” Mathematical Models for Surface Water Hydrology: The Workshop Held at the IBM Scientific Center, Pisa, 9-12 December 1977, pp. 71-108.
S. Harun, N. I. Nor and A. H. M. Kassim, “Artificial Neural Network Model for Rainfall-Runoff Relationship,” Jurnal Teknologi B, Vol. 37, 2002, pp. 1-12.
M. P. Rajurkar, U. C. Kothyari and U. C. Chaube, “Mod-eling of the daily rainfall-runoff relationship with artificial neural network,” Journal of Hydrology, Vol. 285, No. 1-4, 2004, pp. 96-113.
K. M. O'Connor, “Applied Hydrology Deterministic,” Unpublished Lecture Notes, Department of Engineering Hydrology, National University of Ireland, Galway, 1997.
 S. Sanaga and A. Jain, “A Comparative Analysis of Training Methods for Artificial Neural Network Rainfall– Runoff Models,” Applied Soft Computing, Vol. 6, No. 3, 2006, pp. 295-306.
P. K. Swamee, C. S. P. Ojha and A. Abbas, “Mean An-nual Flood Estimation,” Journal of Water Resources Plan-ning and Management, Vol. 121, No. 6, 1995, pp. 403-407.
M. Campolo, P. Andreussi and A. Soldati, “River Flood Forecasting with Neural Network Model,” Water Resource Research, Vol. 35, No. 4, 1999, pp.1191-1197.
B. Zhang and R. S. Govindaraju, “Prediction of Watershed Runoff Using Bayesian Concepts and Modular Neural Networks,” Water Resource Research, Vol. 36, No. 3, 2000, pp. 753-762.
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 Resource Research, Vol. 40, No. 4, 2004, p. 12.
 D. N. Kumar, M. J. Reddy and R. Maity, “Regional Rainfall Forecasting Using Large Scale Climate Tele-connections and Artificial Intelligence Techniques,” Journal of Intelligent Systems, Vol. 16, No. 4, 2007, pp. 307-322.
 H. M. Azmathullah, M. C. Deo and P. B. Deolalikar, “Neural Networks for Estimation of Scour Downstream of a Ski-Jump Bucket,” Journal of Hydraulic Engineering, Vol. 131, No. 10, 2005, pp. 898-908.
 L. M. Tam, A. J. Ghajar and H. K. Tam, “Contribution Analysis of Dimensionless Variables for Laminar and Turbulent Flow Convection Heat Transfer in a Horizontal Tube Using Artificial Neural Network,” Heat Transfer Engineering, Vol. 29, No. 9, 2008 , pp. 793-804.
 M. Aqil, I. Kita, A. Yano and S. Nishiyama, “Comparative Study of Artificial Neural Networks and Neuro- Fuzzy in Continuous Modeling of the Daily and Hourly Behaviour of Runoff,” Journal of Hydrology, Vol. 337, No. 1-2, 2007, pp. 22-34.
 S. M. A. Burney, T. A. Jilani and C. Ardil, “Levenberg- Marquardt Algorithm for Karachi Stock Exchange Share Rates Forecasting,” Proceedings of World Academy of Science, Engineering and Technology, Vol. 3, 2005, pp. 171-176.
 H. Demuth and M. Beale, “Neural Network Toolbox for Use with MATLAB, Users Guide,” Version 3, The MathWorks, Inc., Natick, 1998
 M. T. Hagan and M. B. Menhaj, “Training Feedforward Networks with the Marquardt Algorithm,” IEEE Trans-actions on Neural Networks, Vol. 5, No. 6, 1994, pp. 989- 993.
 K. Ozgur, “Multi-Layer Perceptrons with Levenberg-Marquardt Training Algorithm for Suspended Sediment Concentration Prediction and Estimation,” Hydrological Sciences Journal, Vol. 49, No. 6, 2004, pp. 1025-1040.
 P. Jain, “Modeling of Monthly Rainfall-Runoff Process Using ANN,” Master of Technology Dissertation, De-partment of Civil Engineering, Indian Institute of Technology, Roorkee, 2004.
 V. R. Raju, “Estimation of Monthly Runoff,” Master of Technology Dissertation, Department of Civil Engineering, University of Roorkee, Roorkee, 1998.
 S. K. Jain, P. C. Nayak and K. P. Sudheer, “Models for Estimating Evapotranspiration Using Artificial Neural Networks, and their Physical Interpretation,” Hydrological Processes, Vol. 22, No. 13, 2008, pp. 2225-2234.
 J. E. Nash and J. V. Sutcliffe, “River Flow Forecasting through Conceptual Models, Part 1—A Discussion of Principles,” Journal of Hydrology, Vol. 10, No. 3, 1970, pp. 282-290.