Autoregressive Model for Flood Forecasting

The purpose of the present paper is to develop an autoregressive model for forecasting the frequency of occurrence of flood that India may countenance in a given year. The proposed model being autoregressive, prediction requires no other data than the temporal frequency of occurrence of flood. A comparative study is made among several orders of autoregressive models. The comparison is furnished on the basis of Akaike Information Criteria(AIC) and Bayesian Information Criteria (BIC) statistics for different orders of autoregressive equations. The result of the study reveals the suitability of third order autoregression as a predictive model. The result is qualitatively supported by learning the sample autocorrelations and autocorrelations theoretically implied by third order autoregressive model.
Akaike, H. (1974). A new look at the statistical model identification. IEEE Trans. Autom. Control, AC-19: 716- 723.
Box, G.E.P. and G.M. Jenkins (1976). Time Series Analysis: Forecasting and Control. Holden Day: San Francisco.
Handerson, H.W. and R. Wells (1988). Obtaining attractor dimensions from meteorological time series. Advances in Geophysics, 30: 205-237.
Katz, R.W. (1982). Statistical evaluation of climate experiments with general circulation models: A parametric time series modeling approach. J. Atmos. Sci., 39: 1446- 1455.
Schwarz, G. (1978). Estimating the dimension of a model. Ann. Stat., 6: 461-464.
Sivakumar, B. (2001). Is a chaotic multifractal approach to rainfall possible? Hydrological Processes, 15: 943-955.
Sivakumar, B., Ling, S.Y. and C.Y. Liaw (1998). Evidence of chaotic behaviour in Singapore rainfall. Journal of American Water Resources Association, 34: 301-310.
Stehlik, J. (1999). Deterministic chaos in runoff series. Journal of Hydrology and Hydrodynamics, 47: 271-287.
Wilks, D.S. (1995). Statistical methods in Atmospheric Sciences. Academic Press, New York.