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Development of an ARIMA Model for Monthly Rainfall Forecasting over Khordha District, Odisha, India

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 708)

Abstract

The assessment of climate change, especially in terms of rainfall variability, is of giant concern all over the world at present. Contemplating the high spatiotemporal variation in rainfall distribution, the prior estimation of precipitation is necessary at finer scales too. This study aims to develop an ARIMA model for prediction of monthly rainfall over Khordha district, Odisha, India. Due to the unavailability of recent rainfall data, monthly rainfall records were collected for 1901–2002. The rainfall during 1901–82 was used to train the model and that of 1983–2002 was used for testing and validation purposes. The model selection was made using Akaike information criterion (AIC) and Bayesian information criterion (BIC), and ARIMA (1, 2, 1) (1, 0, 1)12 was found to be the best fit model. The efficiency was evaluated by Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2). The model forecasts produced an excellent match with observed monthly rainfall data. The outstanding accuracy of the model for predicting monthly rainfall for such a long duration of 20 years justifies its future application over the study region, thereby aiding to a better planning and management.

Keywords

  • Rainfall forecast
  • ARIMA
  • Khordha district
  • AIC
  • BIC

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References

  1. Gosain, A.K., Rao, S., Basuray, D.: Climate change impact assessment on hydrology of Indian River Basins. Curr. Sci. 90(3), 346–353 (2006)

    Google Scholar 

  2. Verma, M., Verma, M.K., Swain, S.: Statistical analysis of precipitation over Seonath River Basin, Chhattisgarh, India. Int. J. Appl. Eng. Res. 11(4), 2417–2423 (2016)

    Google Scholar 

  3. Swain, S., Verma, M., Verma, M.K.: Statistical trend analysis of monthly rainfall for Raipur District, Chhattisgarh. Int. J. Adv. Eng. Res. Stud./IV/II/Jan-March, 87–89 (2015)

    Google Scholar 

  4. Swain, S.: Impact of climate variability over Mahanadi river basin. Int. J. Eng. Res. Technol. 3(7), 938–943 (2014)

    Google Scholar 

  5. Narayanan, P., Basistha, A., Sarkar, S., Kamna, S.: Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India. C. R. Geosci. 345(1), 22–27 (2013)

    CrossRef  Google Scholar 

  6. Chattopadhyay, S., Chattopadhyay, G.: Univariate modelling of summer-monsoon rainfall time series: comparison between ARIMA and ARNN. C. R. Geosci. 342(2), 100–107 (2010)

    CrossRef  Google Scholar 

  7. Nanda, S.K., Tripathy, D.P., Nayak, S.K., Mohapatra, S.: Prediction of rainfall in India using Artificial Neural Network (ANN) models. Int. J. Intell. Syst. Appl. 5(12), 1–22 (2013)

    Google Scholar 

  8. Narayanan, P., Sarkar, S., Basistha, A., Sachdeva, K.: Trend analysis and forecast of pre-monsoon rainfall over India. Weather 71(4), 94–99 (2016)

    CrossRef  Google Scholar 

  9. Kaushik, I., Singh, S.M.: Seasonal ARIMA model for forecasting of monthly rainfall and temperature. J. Environ. Res. Dev. 3(2), 506–514 (2008)

    Google Scholar 

  10. Valipour, M., Banihabib, M.E., Behbahani, S.M.R.: Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J. Hydrol. 476, 433–441 (2013)

    CrossRef  Google Scholar 

  11. Khashei, M., Bijari, M.: A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Appl. Soft Comput. 11(2), 2664–2675 (2011)

    CrossRef  Google Scholar 

  12. Valipour, M.: How much meteorological information is necessary to achieve reliable accuracy for rainfall estimations? Agriculture 6(4), 53 (2016)

    CrossRef  Google Scholar 

  13. Salahi, B., Nohegar, A., Behrouzi, M.: The modeling of precipitation and future droughts of Mashhad plain using stochastic time series and Standardized Precipitation Index (SPI). Int. J. Environ. Res. 10(4), 625–636 (2016)

    Google Scholar 

  14. Dastorani, M., Mirzavand, M., Dastorani, M.T., Sadatinejad, S.J.: Comparative study among different time series models applied to monthly rainfall forecasting in semi-arid climate condition. Nat. Hazards 81(3), 1811–1827 (2016)

    CrossRef  Google Scholar 

  15. Rahman, M.A., Yunsheng, L., Sultana, N.: Analysis and prediction of rainfall trends over Bangladesh using Mann–Kendall, Spearman’s rho tests and ARIMA model. Meteorol. Atmos. Phys. 1–16 (2016)

    Google Scholar 

  16. Kumar, U., Jain, V.K.: ARIMA forecasting of ambient air pollutants (O3, NO, NO2 and CO). Stoch. Environ. Res. Risk Assess. 24(5), 751–760 (2010)

    CrossRef  Google Scholar 

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Acknowledgements

The authors are thankful to the organizations (India Meteorological Department and IIT Bombay) for providing valuable data and facilities. We also acknowledge all those persons who have supported for this work.

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Correspondence to S. Swain .

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Swain, S., Nandi, S., Patel, P. (2018). Development of an ARIMA Model for Monthly Rainfall Forecasting over Khordha District, Odisha, India. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 708. Springer, Singapore. https://doi.org/10.1007/978-981-10-8636-6_34

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  • DOI: https://doi.org/10.1007/978-981-10-8636-6_34

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