Environmental Engineering Research

  • 제22권2호
  • /
  • Pages.162-168
  • /
  • 2017
  • /
  • 1226-1025(pISSN)
  • /
  • 2005-968X(eISSN)
대한환경공학회 (Korean Society of Environmental Engineers)
권호 표지
DOI QR코드

DOI QR Code

Monthly rainfall forecast of Bangladesh using autoregressive integrated moving average method

  • Mahmud, Ishtiak (Department of Civil and Environmental Engineering, Shahjalal University of Science and Technology) ;
  • Bari, Sheikh Hefzul (Department of Civil Engineering, Leading University) ;
  • Rahman, M. Tauhid Ur (Department of Civil Engineering, Military Institute of Science and Technology)
  • 투고 : 2016.05.20
  • 심사 : 2016.11.24
  • 발행 : 2017.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

키워드

참고문헌

  1. Box GE, Jenkins GM. Time series analysis: Forecasting and control. Rev. ed. San Francisco: Holden-Day; 1976.
  2. Mishra AK, Desai VR. Drought forecasting using stochastic models. Stoch. Environ. Res Ris. Assess. 2005;19:326-339. https://doi.org/10.1007/s00477-005-0238-4
  3. Rahman M, Islam AHMS, Nadvi SYM, Rahman RM. Comparative study of ANFIS and ARIMA model for weather forecasting in Dhaka. Informatics, Electronics & Vision (ICIEV), 2013 International Conference on, Dhaka; 2013. p. 1-6.
  4. Johnson LA, Montgomery DC. Forecasting and time series analysis. New York: McGraw-Hill; 1976.
  5. Dizon CQ. ARMA modeling of a stochastic process appropriate for the angat reservoir. Philipp. Eng. J. 2007;28:1-20.
  6. Momani PENM. Time series analysis model for rainfall data in Jordan: Case study for using time series analysis. Am. J. Environ. Sci. 2009;5:599-604. https://doi.org/10.3844/ajessp.2009.599.604
  7. Mahsin M, Akhter Y, Begum M. Modeling rainfall in Dhaka division of Bangladesh using time series analysis. J. Math. Model. Appl. 2012;1:67-73.
  8. Bari SH, Rahman MT, Hussain MM, Ray S. Forecasting monthly precipitation in Sylhet city using ARIMA model. Civil Environ. Res. 2015;7:69-77.
  9. Rashid HE. Geography of Bangladesh. 2nd ed. Dhaka: Univ. Press Ltd.; 1991. p. 545.
  10. Shahid S. Rainfall variability and the trends of wet and dry periods in Bangladesh. Int. J. Climatol. 2010;30:2299-2313. https://doi.org/10.1002/joc.2053
  11. You Q, Kang S, Aguilar E, Yan Y. Changes in daily climate extremes in the eastern and central Tibetan Plateau during 1961-2005. J. Geophys. Res-Atmos. 2008;113:D07101.
  12. Mortuza MR, Selmi S, Khudri MM, Ankur AK, Rahman MM. Evaluation of temporal and spatial trends in relative humidity and dew point temperature in Bangladesh. Arab. J Geosci. 2014;7:5037-5050. https://doi.org/10.1007/s12517-013-1139-3
  13. Alexandersson H. A homogeneity test applied to precipitation data. J. Climatol. 1986;6:661-675. https://doi.org/10.1002/joc.3370060607
  14. Von Neumann J. Distribution of the ratio of the mean square successive difference to the variance. Ann. Math. Statist. 1941;12:367-395. https://doi.org/10.1214/aoms/1177731677
  15. Buishand TA. The analysis of homogeneity of long-term rainfall records in the Netherlands. KNMI Scientific Report WR 81-7, De Bilt, The Netherlands; 1981.
  16. Pettitt AN. A non-parametric approach to the change-point problem. J. Roy. Stat. Soc. C-App. 1979;28:126-135.
  17. Box G, Jenkins G, Reinsel G. Time series analysis: Forecasting & control [Internet]. 3rd ed. New Jersy: Prentice Hall; c1994 [cited 23 February 2016]. Available from: http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0130607746.
  18. Chow VT, Maidment DR, Mays LW. Applied hydrology [Internet]. New York: Mcgraw-Hill Book Company; c1988 [cited 23 February 2016]. Available from: http://documentatiecentrum.watlab.be/imis.php?module=ref&refid=127685&basketaction=add.
  19. Ljung GM, Box GEP. On a measure of lack of fit in time series models. Biometrika 1978;65:297-303. https://doi.org/10.1093/biomet/65.2.297

피인용 문헌

  1. Drought forecasting: A review of modelling approaches 2007–2017 pp.2408-9354, 2019, https://doi.org/10.2166/wcc.2019.236
  2. Probabilistic hydrological drought index forecasting based on meteorological drought index using Archimedean copulas vol.50, pp.5, 2017, https://doi.org/10.2166/nh.2019.051
  3. Stochastic Model for Drought Forecasting in the Southern Taiwan Basin vol.11, pp.10, 2017, https://doi.org/10.3390/w11102041
  4. Coupling fuzzy-SVR and boosting-SVR models with wavelet decomposition for meteorological drought prediction vol.78, pp.24, 2017, https://doi.org/10.1007/s12665-019-8700-7
  5. Scale-Dependent Reliability of Projected Rainfalls over Bangladesh with the PRECIS Model vol.8, pp.2, 2017, https://doi.org/10.3390/cli8020020
  6. SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia vol.11, pp.6, 2020, https://doi.org/10.3390/atmos11060602
  7. Use of Teleconnections to Predict Western Australian Seasonal Rainfall Using ARIMAX Model vol.7, pp.3, 2017, https://doi.org/10.3390/hydrology7030052
  8. Medium-term forecasts for groundwater production and rainfall amounts (Deir El-Balah City as a case study) vol.6, pp.5, 2017, https://doi.org/10.1007/s40899-020-00446-z
  9. Verification of Weather and Seasonal Forecast Information Concerning the Peri-Urban Farmers’ Needs in the Lower Ganges Delta in Bangladesh vol.11, pp.10, 2017, https://doi.org/10.3390/atmos11101041
  10. Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction vol.13, pp.15, 2017, https://doi.org/10.3390/w13151997