Anosh Graham*, Nishant Kumar1 Sai and Soham Sahoo2
Department of Environmental Sciences and NRM, College of Forestry, Sam Higginbottom University of Agriculture, Technology & Sciences Allahabad-211007,Uttar Pradesh, India.
1Nishant kumar sai Department of agronomy, RMD College of Agriculture & Research Station, Ambikapur (Surguja), IGKVV Raipur (C.G.) India
3Department of Environmental Sciences and NRM, College of Forestry, Sam Higginbottom University of Agriculture, Technology & Sciences Allahabad-211007,Uttar Pradesh, India.
Email:anoshgraham@gmail.com
Received-07.12.2019, Revised-26.12.2019
Abstract: Precipitation is an important guiding standard for agricultural production; however, it is highly difficult to forecast due to random sequential and seasonal features. Various research groups attempted to predict rainfall on a seasonal time scales using different techniques. This paper describes the Box-Jenkins time series seasonal ARIMA (Auto Regression Integrated Moving Average) approach for prediction of rainfall on monthly scales. ARIMA (1,0,1)(0,1,1) model for rainfall was identified the best model to forecast rainfall for next 4years with confidence level of 95 percent by analyzing last 27 year’s data (1990-2016). Previous years data is used to formulate the seasonal ARIMA model and in determination of model parameters. The performance evaluations of the adopted models are carried out on the basis of correlation coefficient (R2) and root mean square error (RMSE). The study conducted at Ambikapur, Chhattisgarh (India). The results indicate that the ARIMA model provide consistent and satisfactory predictions for rainfall parameters on monthly scale.
Keywords: Rainfall, ARIMA, Correlation Coefficient (R2), Root Mean Square error (RMSE)
ReferenceS
somvanshi, v., pandey, o., agrawal, p., kalanker, n., prakash, m.r. and chand, r. (2006). Modeling and prediction of rainfall using artificial neural network and ARIMA techniques. The Journal of Indian Geophysical Union. Vol. 10. No. 2 p. 141–151.
Box, g. e., jenkins, g.m., reinsel, g.c. and ljung, g.m. (2015). Time series analysis: forecasting and control. 5th ed. John Wiley & Sons. ISBN 1118675029 pp. 712.
Nirmala, M. (2015). Computational models for forecasting annual rainfall in Tamilnadu. Applied Mathematical Sciences. Vol. 9. Iss. 13 p. 617–621.
Box GEP, Jenkins GM. Time series analysis: forecasting and control, Prentince Hall, Inc, 1976, 575.
Nirmala, M. and Sundaram, S.M. (2010). A Seasonal Arima Model for Forecasting monthly rainfall in Tamil Nadu. National. Journal on Advances in Building Sciences and Mechanics. 1(2):43-47.
Etuk, E.H. and Mohamed, T.M. (2014). Time Series Analysis of Monthly Rainfall data for the Gadaref rainfall station, Sudan, by SARIMA Methods. International Journal of Scientific Research in Knowledge. 2(7):320-327.
District Statistical Bulletin, Allahabad (2011). Department of Statistics and Economics, Directorate of Statistics and Planning, Uttar Pradesh, Vikas Bhawan, Allahabad, 46-47.