Development of Flood Forecasting System Using Statistical and ANN Techniques in the Downstream Catchment of Mahanadi Basin, India
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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.
- Bezdeck, J.C, “Pattern recognition with fuzzy objective function algorithm”, Plenum press, New York,1967.
- 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.
- 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.
- 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.
- Central Water Commission of India, “Manual on flood forecasting”, 1980
- 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.
- Cybenko, G., “Approximation by superposition of a sigmoidal function”, Journal of Math. control signal system, Vol.2,1989,pp.303-314.
- Dawson, C.W. and Wilby, R.L., “Hydrological modeling using ANN, Progress in physical geography”, Vol.25, No.1, 2001,pp.80-208.
- 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.
- Ghosh, S.N, “Flood control and drainage engineering” (Text book), 1997.
- Haykin. S., “Neural networks”, Macmillan college publishing company, Engelwood Cliffs, NJ, 1994.
- Hornick, K., Stinchcombe, M. and White, H., “Multilayer feed forward networks are universal approximators”, Neural networks.Vol.2, No. 5, 1989, pp.359-366.
- 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.
- 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.
- K-means,2009, Internet available: http://biocomp.bioen.uiuc.edu/oscar/tools/kmeans.html
- 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.