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Rainfall Forecasting in Northeastern part of Bangladesh Using Time Series ARIMA Model

Author Affiliations

  • 1Department of Civil Engineering, Leading University, Sylhet
  • 2Department of Civil Engineering, Leading University, Sylhet
  • 3Department of Civil Engineering, Leading University, Sylhet
  • 4Department of English, Leading University, Sylhet

Res. J. Engineering Sci., Volume 5, Issue (3), Pages 17-31, March,26 (2016)


To improve water resources management, time series analysis is an important tool. Bangladesh is a densely populated country and now facing shortage of drinking water. Rainwater harvesting is one of the major techniques to overcome this problem. For this purpose, it is very much important to forecast future rainfall events on a monthly basis. Box-Jenkins methodology has been used in this study to build Autoregressive Integrated Moving Average (ARIMA) models for monthly rainfall data from eight rainfall stations in the northeastern part of Bangladesh: (Sunamganj ,Lalakhal, Kanaighat, Sherupur, Chattak, Gobindoganj, Sheola, Zakiganj) for the period 2001-2012. Eight ARIMA models were developed forthe above mentioned stationsas follow: (2,0,2)x(1,0,1)12, (4,0,1)(1,0,0)12, (1,0,1)(0,0,2)12, (1,0,0)(2,0,0)12, (1,0,0)(2,0,0)12, (1,0,0)(2,0,0)12, (1,0,0)(2,0,0)12, (2,0,0)(2,0,0)12 respectively. The performance of the resulting successful ARIMA models were evaluated using the data year (2011). These models were used to forecast the monthly rainfall data for the up-coming years (2013 to 2020). The results supported previous work that had been carried out on the same area recommending the use of water harvesting in both drinking and agricultural practices.


  1. Winstanley D. (1973a)., Recent rainfall trends in Africa, the middle East and India., Nature, 243, 464–465.
  2. Winstanley D. (1973b). Rainfall Patterns and General atmospherically circulation’., Nature, 245, 190–194., undefined
  3. Makridakis S., Wheelwright S.C. and Hyndman R.J. (1998)., Forecasting Methods and Applications., 3rd Edition, John Wiley, New York.
  4. Chiew F.H.S., Stewardson M.J. and McMahon T.A. (1993)., Comparison of six rainfall-runoff modelling approaches., Journal of Hydrology, 147(1), 1-36.
  5. Kuo J.T. and Y.H. Sun (1993)., An intervention model for average 10 day stream flow forecast and synthesis., J. Hydrol., 151, 35-56.
  6. Box G.E.P., Jenkins G.M. and Reinsel G.C. (1994)., Time series analysis: Forecasting and control., Pearson Education, Delhi.
  7. Langu E.M. (1993)., Detection of changes in rainfall and runoff patterns., J. Hydrol., 147, 153-167
  8. Bell W.R. (1984)., An introduction to forecasting with time series models., Insurance: Mathematics and Economics, 3(4), 241-255.
  9. Lee C.M. and Ko C.N. (2011)., Short-term load forecasting using lifting scheme and ARIMA models., Expert Systems with Applications, 38(5), 5902-5911.
  10. Schwarz G. (1978)., Estimating the dimension of a model., The annals of statistics, 6(2), 461-464