Forecasting the monthly inflow rate of the Palar-Porundalar dam in Tamil Nadu using SARIMA model
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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.
Article Details
Article Details
AIC, Correlogram, Forecast, SARIMA, Stationarity test
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