##plugins.themes.bootstrap3.article.main##

Mano Chitra K. Pangayar Selvi R. Mahendran K.

Abstract

Dam inflow forecasting information is essential for planning and management of the dam system. Time series analysis is the most commonly employed technique to forecast the future values based on historical information. In this study, Palar-Porandalar dam in Tamil Nadu inflow series were forecasted in R software package using ARIMA model with seasonal factors. The monthly inflow series of the dam from 2003 January to 2017 December were used as an input source for modeling and forecasting process. Mann-Kendall’s trend test and various Stationarity test were performed to verify the Stationary nature of the data set. From the Correlogram plot, different models were identified; their parameters were optimized and residuals were diagnostically tested using Autocorrelation plot and Ljung Box test. Finally, the best model was selected based on minimum Akaike Information Criteria (AIC), BIC, RMSE and Theil’s U statistic values. From various models, SARIMA (0, 0, 1) (1, 0, 2)12 model was selected as the best one for forecasting the inflow series.

Downloads

Download data is not yet available.

##plugins.themes.bootstrap3.article.details##

##plugins.themes.bootstrap3.article.details##

Keywords

AIC, Correlogram, Forecast, SARIMA, Stationarity test

References
Akaike, H. (1974). A new look at the stationary model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (2008). Time series analysis: forecasting and control, John Wiley and Sons, New Jersey.
Commission Internationale Des Grands Barrages, International Commission on Large Dams (2018). ‘Role of dams’ from www.icold-cigb.org/GB/dams/role_of_dams.asp
Cryer, J.D and Chank, S. (2008). Time series analysis: with applications in R, Springer- New York.
Dickey, D.A and Fuller, W.A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366), 427–431. https://doi.org/10.2307/2286348.
Hamilton, J.D. (1994). Time Series Analysis. Princeton University Press, New Jersey.
Kendall, M. (1975). Multivariate analysis. Charles Griffin and Company, London.
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P and Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54, 159-178. https://doi.org/10.1016/0304-4076 (92)90104-Y.
Ljung, G.M. and Box, G.E.P. (1978). On a measure of a lack of fit in time series models. Biometrika, 65(2), 297–303. https://doi.org/10.1093/biomet/65.2.297.
Moeeni, H., Bonakdari, H. and Ebtehaj, I. (2017). Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach. Indian Academy of Sciences, 126(18). DOI: 10.1007/s12040-017-0798-y.
Phillips, P.C.B and Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335.
Richter, B.D and Thomas, G.A. (2007). Restoring Environmental Flows by Modifying Dam operations. Ecology and Society, 12(1).
Salas, J.D., Delleur, J.W., Yevjevich, V. and Lane, W.L. (1980). Applied modeling of hydrologic time series. Water Resource Publications.
Singh, M., Singh, R. and Shinde, V. (2011). Application of software packages for monthly stream flow forecasting of Kangsabati River in India, International Journal of Computer Applications, 20(3):7–14.
Tadesse, K.B. and Dinka, M.O. (2017). Application of SARIMA model to forecasting monthly flows in Waterval River, South Africa Journal of Water and Land development, 35(X-XII), 229-236. https://doi.org/10.1515/jwld-2017-0088
Citation Format
How to Cite
K., M. C., R., P. S., & K., M. (2019). Forecasting the monthly inflow rate of the Palar-Porundalar dam in Tamil Nadu using SARIMA model. Journal of Applied and Natural Science, 11(2), 375- 378. https://doi.org/10.31018/jans.v11i2.2064
More Citation Formats:
Section
Research Articles