GEP  Vol.3 No.6 , August 2015
Seasonal ARIMA Modeling and Forecasting of Rainfall in Warri Town, Nigeria
Abstract: We obtained historical data of rainfall in Warri Town for the period 2003-2012 for the purpose of model identification and those of 2013 for forecast validation of the identified model. Model identification was by visual inspection of both the sample ACF and sample PACF to postulate many possible models and then use the model selection criterion of Residual Sum of Square (RSS), Akaike’s Information Criterion (AIC) complemented by the Schwartz’s Bayesian Criterion (SBC), to choose the best model. The chosen model was the Seasonal ARIMA (1, 1, 1) (0, 1, 1) process which met the criterion of model parsimony with RSS value of 81.098,773, AIC value of 281.312,35 and SBC value of 289.330,84. Model adequacy checks showed that the model was appropriate. We used the model to forecast rainfall for 2013 and the result compared very well with the observed empirical data for 2013.
Keywords: ACF, PACF, ARIMA, Rainfall, AIC, SBC
Cite this paper: Eni, D. and Adeyeye, F. (2015) Seasonal ARIMA Modeling and Forecasting of Rainfall in Warri Town, Nigeria. Journal of Geoscience and Environment Protection, 3, 91-98. doi: 10.4236/gep.2015.36015.

[1]   French, M.N., Krajewski, W.F. and Cuykendall, R.R. (1992) Rainfall Forecasting in Space and Time Using Neural Network. Journal of Hydrology, 137, 1-31.

[2]   Gwangseob, K. and Ana, P.B. (2001) Quantitative Flood Forecasting Using Multisensor Data and Neural Networks. Journal of Hydrology, 246, 45-62.

[3]   Hung, N.Q., Babel, M.S., Weesakul, S. and Tri-pathi, N.K. (2008) An Artificial Neural Network Model for Rainfall Forecasting in Bangkok, Thailand. Hydrology and Earth System Sciences, 5, 183-218.

[4]   James, L.D. and Thompson, W.O. (1970) Least Square Estimation of Constants in a Linear Recession Model. Water Resources Research, 64, 1062-1069.

[5]   Fiering, M.B. and Jackson, B.B. (1971) Synthetic Streamflows. Water Resources Monograph No.1, America Geophysical Union, Washington DC.

[6]   Bras, R.L. and Rodri-guez-Iturbe, I. (1993) Random Functions and Hydrology. Dover, New York

[7]   Chang, F.J. and Chen, Y.C. (2001) A Counter Propagation Fuzy-Neural Network Modeling Approach to Real Time Streamflow Prediction. Journal of Hydrology, 245, 153-164.

[8]   Farahmand, T., Fleming, S.W. and Quilty, E.J. (2007) Detection and Visualization of Storm Hydrograph Changes under Urbanization: An Impulse Response Approach. Journal of Environmental Management, 85, 93-100.

[9]   McLeod, A. and Lennox, W. (1977) Advances in Box-Jenkins Modeling: Model Construction. Water Resources Research, 13, 577-586.

[10]   Mcleod, A.I. (1995) Diagnostic Checking of Periodic Autore-gression Models With Application. The Journal of Time Series Analysis, 15, 221-233.

[11]   Brinskiene, M. and Rudzkiene, V. (2005) Modeling and Forecasting of Country Tourism Development in Lithuania. Journal of Environmental Engineering and Landscaping Management, 13, 116-120.

[12]   Brooks, C. (2002) Introductory Econometrics for Finance. Cambridge University Press, UK.

[13]   Akaike, H. (1974) A New Look at the Statistical Model Identification. IEEE Transaction on Automatic Control, 19, 716-723.

[14]   Schwartz, G.E. (1978) Estimating the Dimension of a Model. Annals of Statistics, 6, 461-464.

[15]   Melard, G. (1984) A Fast Algorithm for the Exact Likelihood of Autoregressive-Moving Average Models. Applied Statistician, 33, 104-114.

[16]   Brockwell, P.J. and Davis, R.A. (1991) Time Series: Theory and Method. Springer.

[17]   Anderson, R.L. (1942) Distribution of Serial Correlation Coefficient. Annals of Mathematical Statistics, 13, 1-13.