Development of Flood Forecasting System Using Statistical and ANN Techniques in the Downstream Catchment of Mahanadi Basin, India
ABSTRACT
The floods in river Mahanadi delta are due to either dam release of Hirakud or due to contribution of intercepted catchment between Hirakud dam and delta. It is seen from post-Hirakud periods (1958) that out of 19 floods 14 are due to intercepted catchment contribution. The existing flood forecasting systems are mostly for upstream catchment, forecasting the inflow to reservoir, whereas the downstream catchment is devoid of a sound flood forecasting system. Therefore, in this study an attempt has been made to develop a workable forecasting system for downstream catchment. Instead of taking the flow time series concurrent flood peaks of 12 years of base and forecasting stations with its corresponding travel time are considered for analysis. Both statistical method and ANN based approach are considered for finding the peak to reach at delta head with its corresponding travel time. The travel time has been finalized adopting clustering techniques, there by differentiating high, medium and low peaks. The method is simple and it does not take into consideration the rainfall and other factors in the intercepted catchment. A comparison between both methods are tested and it is found that the ANN methods are better beyond the calibration range over statistical method and the efficiency of either methods reduces as the prediction reach is extended. However, it is able to give the peak discharge at delta head before 24 hour to 37 hour for high to low peaks.
The floods in river Mahanadi delta are due to either dam release of Hirakud or due to contribution of intercepted catchment between Hirakud dam and delta. It is seen from post-Hirakud periods (1958) that out of 19 floods 14 are due to intercepted catchment contribution. The existing flood forecasting systems are mostly for upstream catchment, forecasting the inflow to reservoir, whereas the downstream catchment is devoid of a sound flood forecasting system. Therefore, in this study an attempt has been made to develop a workable forecasting system for downstream catchment. Instead of taking the flow time series concurrent flood peaks of 12 years of base and forecasting stations with its corresponding travel time are considered for analysis. Both statistical method and ANN based approach are considered for finding the peak to reach at delta head with its corresponding travel time. The travel time has been finalized adopting clustering techniques, there by differentiating high, medium and low peaks. The method is simple and it does not take into consideration the rainfall and other factors in the intercepted catchment. A comparison between both methods are tested and it is found that the ANN methods are better beyond the calibration range over statistical method and the efficiency of either methods reduces as the prediction reach is extended. However, it is able to give the peak discharge at delta head before 24 hour to 37 hour for high to low peaks.
Cite this paper
nullA. Karl and A. Lohani, "Development of Flood Forecasting System Using Statistical and ANN Techniques in the Downstream Catchment of Mahanadi Basin, India," Journal of Water Resource and Protection, Vol. 2 No. 10, 2010, pp. 880-887. doi: 10.4236/jwarp.2010.210105.
nullA. Karl and A. Lohani, "Development of Flood Forecasting System Using Statistical and ANN Techniques in the Downstream Catchment of Mahanadi Basin, India," Journal of Water Resource and Protection, Vol. 2 No. 10, 2010, pp. 880-887. doi: 10.4236/jwarp.2010.210105.
References
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[1] Bezdeck, J.C, “Pattern recognition with fuzzy objective function algorithm”, Plenum press, New York,1967. [2] Bruen,M. and Yang,J., “Functional networks in real-time flood forecasting-a novel application.” Journal of Advances in Water Resources, Vol.28, No.9,2005, pp.899-909. [3] Campolo M., Andreeussi P. and Soldati A., “River flood forecasting with a neural network model”. Water resources research, Vol.35, No.4, 1999, pp.1191-1197. [4] Campolo M., Soldati A. and Andreeussi P., “Artificial neural network approach to flood forecasting in the River Arno”. Hydrological Sciences Journal, Vol.48, No.4, 2003, pp.381-398. [5] Central Water Commission of India, “Manual on flood forecasting”, 1980 [6] Coulibaly, P., Anctil, F. and Bobee, B., “Daily reservoir inflow forecasting using artificial neural network with stopped training approach”, Journal of Hydrology,Vol.230,No.(3-4),2000,pp.244-257. [7] Cybenko, G., “Approximation by superposition of a sigmoidal function”, Journal of Math. control signal system, Vol.2,1989,pp.303-314. [8] Dawson, C.W. and Wilby, R.L., “Hydrological modeling using ANN, Progress in physical geography”, Vol.25, No.1, 2001,pp.80-208. [9] Dharmasena, G.T., “Application of mathematical models for flood forecasting in Sri Lanka, Destructive water: Water caused natural disasters, their abatement and control ,” (Proceeding of the conference held at Anaheim, California, June 1996,IAHS Publ.No.239,1997. [10] Ghosh, S.N, “Flood control and drainage engineering” (Text book), 1997. [11] Haykin. S., “Neural networks”, Macmillan college publishing company, Engelwood Cliffs, NJ, 1994. [12] Hornick, K., Stinchcombe, M. and White, H., “Multilayer feed forward networks are universal approximators”, Neural networks.Vol.2, No. 5, 1989, pp.359-366. [13] Imrie, C.E., Durucan, S. and Korre, A., “River flow prediction using artificial neural network: Generalization beyond the calibration range”, Journal of Hydrology,Vol.233,No.1-4,pp.138-153. [14] Karunanithi, N., Granny, W.J, Whitely,D. and Bovee, K., “Neural networks for river flow prediction”, Journal of computing in Civil Engineering,ASCE,Vol.8,No.2,1994,pp.201-220. [15] K-means,2009, Internet available: http://biocomp.bioen.uiuc.edu/oscar/tools/kmeans.html [16] Lekkas, D.F., Onof, C., Lee, M.J. and Baltas, E.A., “Application of neural networks for flood forecasting”, Global Nest: the Int. Journal. Vol.6, No.3, 2004, pp.205-211. [17] Lohani, A.K, “ANN and Fuzzy logic in hydrological modeling and flow forecasting”, Ph.D thesis, Indian Institute of Technology,Roorkee,2007. [18] MacQueen,J.B., “Some methods for classification of multivariate observation,”Proceeding of 5th Berkely Symp. on Probability and Statistics, University of California Press, Berkely,1967,pp.281-297. [19] Minns, A. W., and Hall, M. J. (1996) “Artificial neural networks as rainfall-runoff models”, Hydrol. Sci. J., Vol. 41(3), pp. 399-417 [20] Muhamad, J.R. and Hassan, J.N., “Khabur river flow modeling using artificial neural network”. Al_ Rafidain Engineering., Vol.13, No.2, 2005, pp.33-42. [21] Priddy, L.K. and Keller, E.P., “Artificial neural networks, an introduction”, SPIE Press, Bellingham, Washington, 2005. [22] Solomatine, D.P. and Xue, Y., “M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai river in China”, Journal of Hydrologic Engineering, Vol.9, No. (6),2004, pp.491-501. [23] Solomatine, D.P. and Price R.K., “Innovative approaches to flood forecasting using data driven and hybrid modeling, 6th International Conference on Hydroinformatics – Liong”. [24] Spokkereff, E., “Flood forecasting for the river Rhine in the Netherlands”, The extreme of extremes: Extraordinary floods, (Proceeding of the symposium, Reykjavic, Ireland), IAHS Publ. No.271, 2002, pp.347-352. [25] Water Resources Department, Government of Orissa, “Time of Flow in Mahanadi River between Hirakud & Mundali” 2009. Internet available :http://www.dowrorissa.gov.in/Flood/DailyFloodBulletin.htm, [26] World Meteorological Organization (WMO),”Guide for hydrological practices”, Geneva, Switzerland, 1994. [27] Xiongrui, Y., Jun, X. and Xiang, Z., “Flood forecasting by coupling clustering methods and artificial neural networks”, Proceeding of symposium HS 3006,IUGG, Perugia,Italy,2007. [28] Zealand, C.M, Burn, D.H. and Simonovic, S.P., “Short term streamflow forecasting using artificial neural network”, Journal of Hydrology, Vol.214,1999, pp.32-48. [29] Zhang, J. and Hall, M. J., “Regional flood frequency analysis for the Gan-Ming river basin in China”, Journal of Hydrology, Vol. 296, No.(1-4),2004,pp.98-117. [30] Mukerjee, A., Chatterji, C., Singh, N. and Raghuvanshi, N.S., ”Flood forecasting using ANN, Neuro-Fuzzy and Neuro-GA models”,Vol.14,No.6,2009,pp.647-652. [31] Neural network toolbox, Matlab,2004.