Artificial Neural Networks for Event Based Rainfall-Runoff Modeling
ABSTRACT
The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables
The Artificial Neural Network (ANN) approach has been successfully used in many hydrological studies especially the rainfall-runoff modeling using continuous data. The present study examines its applicability to model the event-based rainfall-runoff process. A case study has been done for Ajay river basin to develop event-based rainfall-runoff model for the basin to simulate the hourly runoff at Sarath gauging site. The results demonstrate that ANN models are able to provide a good representation of an event-based rainfall-runoff process. The two important parameters, when predicting a flood hydrograph, are the magnitude of the peak discharge and the time to peak discharge. The developed ANN models have been able to predict this information with great accuracy. This shows that ANNs can be very efficient in modeling an event-based rainfall-runoff process for determining the peak discharge and time to the peak discharge very accurately. This is important in water resources design and management applications, where peak discharge and time to peak discharge are important input variables
Cite this paper
A. Sarkar and R. Kumar, "Artificial Neural Networks for Event Based Rainfall-Runoff Modeling," Journal of Water Resource and Protection, Vol. 4 No. 10, 2012, pp. 891-897. doi: 10.4236/jwarp.2012.410105.
A. Sarkar and R. Kumar, "Artificial Neural Networks for Event Based Rainfall-Runoff Modeling," Journal of Water Resource and Protection, Vol. 4 No. 10, 2012, pp. 891-897. doi: 10.4236/jwarp.2012.410105.
References
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Kalteh, “Rainfall-Runoff Using Artificial Neural Networks (ANNs) and Understanding,” Caspian Journal of Environmental Science, Vol. 6, No. 1, 2008. pp. 53-58. [20] R. Modarres, “Multi-Criteria Validation of Artificial Neural Network Rainfall-Runoff,” Hydrology and Earth System Sciences, Vol. 13, 2009, pp. 411-421. doi:10.5194/hess-13-411-2009 [21] A. Dorum, A. Yarar, M. F. Sevimli and M. Onucyildiz, “The Rainfall-Runoff Data of Susurluk Basin,” Expert Systems with Applications: An International Journal, Vol. 37, No. 9, 2010, pp. 6587-6593. [22] B. Yegnanarayana, “Artificial Neural Networks,” Prentice-Hall of India Pvt. Ltd., New Delhi, 1999. [23] Neural Power, “Neural Networks Professional Version 2.0,” CPC-X Software, 2003. [24] N. Karunanithi, W. J. Grenney, D. Whitley and K. Bovee, “Neural Networks for River Flow Prediction,” Journal of Computing in Civil Engineering, ASCE, Vol. 8, No. 2, 1994, pp. 201-220. doi:10.1061/(ASCE)0887-3801(1994)8:2(201) [25] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning Internal Representations by Error Propagation: Parallel Distributed Processing, Vol. I,” MIT Press, Cambridge, 1986, pp. 318-362. [26] K. Hsu, H. V. Gupta and S. Sorooshian, “Artificial Neural Network of the Rainfall-Runoff Process,” Water Resources Research, Vol. 31, No. 10, 1995, pp. 2517-2530. doi:10.1029/95WR01955
[1] 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 [2] R. B. Grayson, I. D. Moore and T. A. McMahon, “Physically Based Hydrologic-2. Is the Concept Realistic?” Water Resources Research, Vol. 28, No. 10, 1992, pp. 2659-2666. doi:10.1029/92WR01259 [3] K. Chakraborty, K. Mehrotra, C. K. Mohan and S. Ranka, “Neural Networks and Their Applications,” Review of Scientific Instruments, Vol. 65, 1992, pp. 1803-1832. [4] D. Hammerstrom, “Neural Networks at Work,” IEEE Spectrum, Vol. 30, No. 7, 1993, pp. 46-53. doi:10.1109/6.222230 [5] S. Haykin, “Neural Networks—A Comprehensive Foundation,” Macmillan, New York, 1994. [6] A. H. Halff, H. M. Halff and M. Azmoodeh, “Predicting Runoff from Rainfall Using Neural Network,” Proceedings Engineering Hydrolgy, American Society of Civil Engineers, New York, 1993, pp. 760-765. [7] A.T. Hjelmfelt and M. Wang, “Artificial Neural Networks as Unit Hydrograph applications,” Proceedings Engineering Hydrolgy, American Society of Civil Engineers, New York, 1998, pp. 760-765. [8] M. Zhu, M. Fujita and N. Hashimoto, “Application of Neural Networks to Runoff Prediction,” In: K. W. Hipel, et al., Eds., Stochastic and Statistical Method in Hydrology and Environmental Engineering, Vol. 3, Kluwer, Dordrecht, 1994, pp. 205-216. [9] J. Smith and R. N. Eli, “Neural Network Models of Rainfall-Runoff Processes,” Journal of Water Resources Planning & Management, American Society of Civil Engineers, Vol. 121, No. 6, 1995, pp. 499-508. doi:10.1061/(ASCE)0733-9496(1995)121:6(499) [10] P. Carriere, S. Mohaghegh and R. Gaskari, “Performance of a Virtual Runoff Hydrograph System,” Journal of Computing in Civil Engineering, American Society of Civil Engineers, Vol. 122, No. 6, 1996, pp. 421-427. [11] N. T. G. Lange, “Advantages of Unit Hydrograph Derivation by Neural Networks,” In: V. Babovic and C. L. Larsen, Eds., Hydroinformatics, Vol. 2, Balkema, Rotterdam, 1998. [12] J. Anmala, B. Zhang and R. S. Govindraju, “Comparison of ANNs and Empirical Approaches for Predicting Watershed Runoff,” Journal of Water Resources Planning & Management, American Society of Civil Engineers, Vol. 126, No. 3, 2000, pp. 156-166. doi:10.1061/(ASCE)0733-9496(2000)126:3(156) [13] 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 [14] R. Chibanga, J. Berlamont and J. Vandewalle, “Modelling and Forecasting of Hydrological Variables Using Artificial Neural Networks: The Kafue River Sub-Basin,” Hydrological Sciences Journal, Vol. 48, No. 3, 2003, pp. 363-379. doi:10.1623/hysj.48.3.363.45282 [15] Y. Chiang, L. Chang and F. Chang, “Comparison of Static Feed Forward and Dynamic-Feedback Neural Networks for Rainfall-Runoff,” Journal of Hydrology, Vol. 290, 2004, pp. 297-211 doi:10.1016/j.jhydrol.2003.12.033 [16] J. S. Wu, P. E. Han, J. Annambhotla and S. Bryant, “Artificial Neural Networks for Forecasting Watershed Runoff and Stream Flows,” Journal of Hydrologic Engineering, ASCE, Vol. 10, No. 3, 2005, pp. 216-222. doi:10.1061/(ASCE)1084-0699(2005)10:3(216) [17] A. Sarkar, A. Agarwal and R. D. Singh, “Artificial Neural Network Models for Rainfall-Runoff Forecasting in a Hilly Catchment,” Journal of Indian Water Resources Society, Vol. 26, No. 3-4, 2006, pp. 1-4. [18] O. Kisi, “Streamflow Forecasting Using Different Artificial Neural Network Algorithms,” Journal of Hydrologic Engineering, ASCE, Vol. 12, No. 5, pp. 532-539. doi:10.1061/(ASCE)1084-0699(2007)12:5(532) [19] A. M. Kalteh, “Rainfall-Runoff Using Artificial Neural Networks (ANNs) and Understanding,” Caspian Journal of Environmental Science, Vol. 6, No. 1, 2008. pp. 53-58. [20] R. Modarres, “Multi-Criteria Validation of Artificial Neural Network Rainfall-Runoff,” Hydrology and Earth System Sciences, Vol. 13, 2009, pp. 411-421. doi:10.5194/hess-13-411-2009 [21] A. Dorum, A. Yarar, M. F. Sevimli and M. Onucyildiz, “The Rainfall-Runoff Data of Susurluk Basin,” Expert Systems with Applications: An International Journal, Vol. 37, No. 9, 2010, pp. 6587-6593. [22] B. Yegnanarayana, “Artificial Neural Networks,” Prentice-Hall of India Pvt. Ltd., New Delhi, 1999. [23] Neural Power, “Neural Networks Professional Version 2.0,” CPC-X Software, 2003. [24] N. Karunanithi, W. J. Grenney, D. Whitley and K. Bovee, “Neural Networks for River Flow Prediction,” Journal of Computing in Civil Engineering, ASCE, Vol. 8, No. 2, 1994, pp. 201-220. doi:10.1061/(ASCE)0887-3801(1994)8:2(201) [25] D. E. Rumelhart, G. E. Hinton and R. J. Williams, “Learning Internal Representations by Error Propagation: Parallel Distributed Processing, Vol. I,” MIT Press, Cambridge, 1986, pp. 318-362. [26] K. Hsu, H. V. Gupta and S. Sorooshian, “Artificial Neural Network of the Rainfall-Runoff Process,” Water Resources Research, Vol. 31, No. 10, 1995, pp. 2517-2530. doi:10.1029/95WR01955