| Time-series analysis of monthly rainfall data for theMahanadi River Basin, India |
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| Citation of this paper:Janhabi Meher,Ramakar Jha,2013.Time-series analysis of monthly rainfall data for theMahanadi River Basin, India.Sciences in Cold and Arid Regions,5(1):0073~0084. |
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| Abstract:Time series analysis has two goals, modeling random mechanisms and predicting future series using historical data. In the present
work, a uni-variate time series autoregressive integrated moving average (ARIMA) model has been developed for (a) simulating
and forecasting mean rainfall, obtained using Theissen weights; over the Mahanadi River Basin in India, and (b) simulating and
forecasting mean rainfall at 38 rain-gauge stations in district towns across the basin. For the analysis, monthly rainfall data of each
district town for the years 1901–2002 (102 years) were used. Theissen weights were obtained over the basin and mean monthly
rainfall was estimated. The trend and seasonality observed in ACF and PACF plots of rainfall data were removed using power
transformation (α=0.5) and first order seasonal differencing prior to the development of the ARIMA model. Interestingly, the
ARIMA model (1,0,0)(0,1,1)12 developed here was found to be most suitable for simulating and forecasting mean rainfall over the
Mahanadi River Basin and for all 38 district town rain-gauge stations, separately. The Akaike Information Criterion (AIC), goodness
of fit (Chi-square), R2 (coefficient of determination), MSE (mean square error) and MAE (mea absolute error) were used to
test the validity and applicability of the developed ARIMA model at different stages. This model is considered appropriate to
forecast the monthly rainfall for the upcoming 12 years in each district town to assist decision makers and policy makers establish
priorities for water demand, storage, distribution, and disaster management. |
| keywords:Akaike Information Criterion autoregressive integrated moving average model goodness of fit rainfall forecasting |
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