Indian Journal of Science and Technology
Year: 2018, Volume: 11, Issue: 48, Pages: 1-9
M. Nirmala1 * and Tariq Mahgoub Mohamed2
1 Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai – 600119, Tamil Nadu, India; [email protected]
2 Department of Civil Engineering, Jazan University, Jazan, KSA; [email protected]
*Author for correspondence
Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai – 600119, Tamil Nadu, India; [email protected]
Objectives: In this work, the Box-Jenkins approach, which known as Seasonal Autoregressive Integrated Moving Average Model (SARIMA) model, was applied to predict monthly rainfall in Kordofan state, Sudan. Methods/Statistical Analysis: Using the stochastic models to predict monthly rainfall is an important issue for planning many water resources projects. The monthly rainfall data were obtained from the Sudan Meteorological Authority, covering the period 1971-2010. Findings: Test of the original data displays horizontal trend and seasonal periodicity. The data is checked for non stationarity through Augmented Dickey- Fuller Unit Root Test (ADF). The Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) were used to identify the seasonality and it was removed by employing first order seasonal differencing. The SARIMA (0,0,1)x(0,1,1)12 model was selected to be most proper for predicting monthly rainfall. Application/Improvements: This model may be applied as a foundation for monthly rainfall Predicting in Kordofan state.
Keywords: Rainfall, Seasonality, Seasonal Prediction, Box – Jenkins SARIMA, Stationary
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