Skip to main content
Log in

Forecasting Southwest Indian Monsoon Rainfall Using the Beta Seasonal Autoregressive Moving Average (\(\beta\)SARMA) Model

  • Published:
Pure and Applied Geophysics Aims and scope Submit manuscript

Abstract

Monsoon rainfall has always been a key driver of the Indian economy. Understanding the spatiotemporal rainfall pattern and its prediction is therefore of great importance. The present endeavor implements a beta seasonal autoregressive moving average (BSARMA) model to forecast the southwest monsoon rainfall (June–September) in five homogeneous regions of the Indian subcontinent. The southwest monsoon rainfall data set for the period 1871–2016 was utilized to conduct the analysis. The best-fit model for each region was selected on the basis of diagnostic analysis and the Akaike information criterion (AIC). Further, the conditional maximum likelihood estimator was employed to compute the parameters of the models. The accuracy of the models was assessed using root mean square error (RMSE), mean absolute error (MAE), and mean absolute scaled error (MASE). The study demonstrates that the BSARMA model outperforms the seasonal autoregressive integrated moving average (SARIMA) model in all three measures.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Adams, S. O., & Bamanga, M. A. (2020). Modelling and forecasting seasonal behavior of rainfall in Abuja, Nigeria; A SARIMA approach. American Journal of Mathematics and Statistics, 10(1), 10–19.

    Google Scholar 

  • Ahmed, N. K., Atiya, A. F., Gayar, N. E., & El-Shishiny, H. (2010). An empirical comparison of machine learning models for time series forecasting. Econometric Reviews, 29(5–6), 594–621.

    Article  Google Scholar 

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.

    Article  Google Scholar 

  • Andersen, E. B. (1970). Asymptotic properties of conditional maximum-likelihood estimators. Journal of the Royal Statistical Society: Series B (Methodological), 32(2), 283–301.

    Google Scholar 

  • Bayer, F. M., Cintra, R. J., & Cribari-Neto, F. (2018). Beta seasonal autoregressive moving average models. Journal of Statistical Computation and Simulation, 88(15), 2961–2981.

    Article  Google Scholar 

  • Benjamin, M. A., Rigby, R. A., & Stasinopoulos, D. M. (2003). Generalized autoregressive moving average models. Journal of the American Statistical Association, 98(461), 214–223.

    Article  Google Scholar 

  • Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.

    Google Scholar 

  • Briët, O. J., Amerasinghe, P. H., & Vounatsou, P. (2013). Generalized seasonal autoregressive integrated moving average models for count data with application to malaria time series with low case numbers. PloS One, 8(6), e65761.

    Article  Google Scholar 

  • Brockwell, P. J., & Davis, R. A. (1991). Time series: theory and methods. Berlin: Springer-Verlag.

    Book  Google Scholar 

  • Chattopadhyay, S., & Chattopadhyay, G. (2010). Univariate modelling of summer-monsoon rainfall time series: comparison between ARIMA and ARNN. Comptes Rendus Geoscience, 342(2), 100–107.

    Article  Google Scholar 

  • Chuang, M. D., & Yu, G. H. (2007). Order series method for forecasting non-Gaussian time series. Journal of Forecasting, 26(4), 239–250.

    Article  Google Scholar 

  • Dankwa, P., Cudjoe, E., Amuah, E. E. Y., Kazapoe, R. W., & Agyemang, E. P. (2021). Analyzing and forecasting rainfall patterns in the Manga-Bawku area, northeastern Ghana: Possible implication of climate change. Environmental Challenges, 5, 100354.

    Article  Google Scholar 

  • Dash, Y., Mishra, S. K., & Panigrahi, B. K. (2018). Rainfall prediction for the Kerala state of India using artificial intelligence approaches. Computers & Electrical Engineering, 70, 66–73.

    Article  Google Scholar 

  • Dimri, T., Ahmad, S., & Sharif, M. (2020). Time series analysis of climate variables using seasonal ARIMA approach. Journal of Earth System Science, 129(1), 1–16.

    Article  Google Scholar 

  • Eni, D., et al. (2015). Seasonal ARIMA modeling and forecasting of rainfall in Warri Town, Nigeria. Journal of Geoscience and Environment Protection, 3(06), 91.

    Article  Google Scholar 

  • Farajzadeh, J., Fard, A. F., & Lotfi, S. (2014). Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network’’ and “time series analysis’’ model. Water Resources and Industry, 7, 38–48.

    Article  Google Scholar 

  • Ferrari, S., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7), 799–815.

    Article  Google Scholar 

  • Fletcher, S., & Ponnambalam, K. (1996). Estimation of reservoir yield and storage distribution using moments analysis. Journal of Hydrology, 182(1–4), 259–275.

    Article  Google Scholar 

  • Ghamariadyan, M., & Imteaz, M. A. (2021). Monthly rainfall forecasting using temperature and climate indices through a hybrid method in Queensland, Australia. Journal of Hydrometeorology, 22(5), 1259–1273.

    Google Scholar 

  • Guhathakurta, P., Sreejith, O., & Menon, P. (2011). Impact of climate change on extreme rainfall events and flood risk in India. Journal of Earth System Science, 120(3), 359–373.

    Article  Google Scholar 

  • Guolo, A., & Varin, C. (2014). Beta regression for time series analysis of bounded data, with application to Canada Google® flu trends. The Annals of Applied Statistics, 8(1), 74–88.

    Article  Google Scholar 

  • Janacek, G., & Swift, A. (1990). A class of models for non-normal time series. Journal of Time Series Analysis, 11(1), 19–31.

    Article  Google Scholar 

  • Kashid, S. S., & Maity, R. (2012). Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using genetic programming. Journal of Hydrology, 454, 26–41.

    Article  Google Scholar 

  • Krishna Kumar, K., Rupa Kumar, K., Ashrit, R., Deshpande, N., & Hansen, J. W. (2004). Climate impacts on Indian agriculture. International Journal of Climatology: A Journal of the Royal Meteorological Society, 24(11), 1375–1393.

    Article  Google Scholar 

  • Kumar, D., Singh, A., Samui, P., & Jha, R. K. (2019). Forecasting monthly precipitation using sequential modelling. Hydrological Sciences Journal, 64(6), 690–700.

    Article  Google Scholar 

  • Lama, A., Singh, K., Singh, H., Shekhawat, R., Mishra, P., & Gurung, B. (2021). Forecasting monthly rainfall of sub-Himalayan region of India using parametric and non-parametric modelling approaches. Modeling Earth Systems and Environment, 8, 837–845.

    Article  Google Scholar 

  • Li, W. K., & McLeod, A. I. (1988). ARMA modelling with non-Gaussian innovations. Journal of Time Series Analysis, 9(2), 155–168.

    Article  Google Scholar 

  • Loo, Y. Y., Billa, L., & Singh, A. (2015). Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in southeast asia. Geoscience Frontiers, 6(6), 817–823.

    Article  Google Scholar 

  • Luk, K. C., Ball, J. E., & Sharma, A. (2001). An application of artificial neural networks for rainfall forecasting. Mathematical and Computer modelling, 33(6–7), 683–693.

    Article  Google Scholar 

  • Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PloS One, 13(3), e0194889.

    Article  Google Scholar 

  • McLeod, A. I., & Li, W. K. (1983). Diagnostic checking ARMA time series models using squared-residual autocorrelations. Journal of Time Series Analysis, 4(4), 269–273.

    Article  Google Scholar 

  • Menon, A., Levermann, A., & Schewe, J. (2013). Enhanced future variability during India’s rainy season. Geophysical Research Letters, 40(12), 3242–3247.

    Article  Google Scholar 

  • Moriña, D., Puig, P., Ríos, J., Vilella, A., & Trilla, A. (2011). A statistical model for hospital admissions caused by seasonal diseases. Statistics in Medicine, 30(26), 3125–3136.

    Article  Google Scholar 

  • Murthy, K. N., Saravana, R., & Kumar, K. V. (2018). Modeling and forecasting rainfall patterns of southwest monsoons in north-east India as a SARIMA process. Meteorology and Atmospheric Physics, 130(1), 99–106.

    Article  Google Scholar 

  • Narasimha Murthy, K. V., & Kishore Kumar, G. (2022). Distribution and prediction of monsoon rainfall in homogeneous regions of India: A stochastic approach. Pure and Applied Geophysics, 179, 2577–2590.

    Article  Google Scholar 

  • Narayanan, P., Basistha, A., Sarkar, S., & Kamna, S. (2013). Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India. Comptes Rendus Geoscience, 345(1), 22–27.

    Article  Google Scholar 

  • Nourani, V., Alami, M. T., & Aminfar, M. H. (2009). A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence, 22(3), 466–472.

    Article  Google Scholar 

  • Ogundari, K., & Onyeaghala, R. (2021). The effects of climate change on African agricultural productivity growth revisited. Environmental Science and Pollution Research, 28, 30035–30045.

    Article  Google Scholar 

  • Parthasarathy, B. (1995). Monthly and seasonal rainfall series for all India, homogeneous regions and meteorological subdivisions: 1871-1994. Indian Institute of Tropical Meteorology Research Report.

  • Parthasarathy, B., Kumar, R., & Munot, A. (1996). Homogeneous regional summer monsoon rainfall over India: Interannual variability, teleconnections.

  • Parthasarathy, B., Munot, A., & Kothawale, D. (1994). All-India monthly and seasonal rainfall series: 1871–1993. Theoretical and Applied Climatology, 49(4), 217–224.

    Article  Google Scholar 

  • Polisetty, K., & Ebenezer, A. Y. (2021). An empirical study on rainfall patterns of monsoon season in the north-west India using time series models. Journal of Statistics and Management Systems, 24(3), 559–572.

    Article  Google Scholar 

  • Praveen, B., Talukdar, S., Mahato, S., Mondal, J., Sharma, P., Islam, A. R. M. T., et al. (2020). Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Scientific Reports, 10(1), 1–21.

    Article  Google Scholar 

  • Rajeevan, M., Bhate, J., & Jaswal, A. K. (2008). Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data. Geophysical Research Letters. https://doi.org/10.1029/2008GL035143.

    Article  Google Scholar 

  • Ramesh, K., & Iyengar, R. (2016). New ANN model for forecasting Indian monsoon rainfall. Natural Hazards.

  • Requena, A. I., Nguyen, T. H., Burn, D. H., Coulibaly, P., et al. (2021). A temporal downscaling approach for sub-daily gridded extreme rainfall intensity estimation under climate change. Journal of Hydrology: Regional Studies, 35, 100811.

    Google Scholar 

  • Reshma, T., Varikoden, H., & Babu, C. (2021). Observed changes in Indian summer monsoon rainfall at different intensity bins during the past 118 years over five homogeneous regions. Pure and Applied Geophysics, 178(9), 3655–3672.

    Article  Google Scholar 

  • Rocha, A. V., & Cribari-Neto, F. (2009). Beta autoregressive moving average models. Test, 18(3), 529–545.

    Article  Google Scholar 

  • Roxy, M. K., Ghosh, S., Pathak, A., Athulya, R., Mujumdar, M., Murtugudde, R., et al. (2017). A threefold rise in widespread extreme rain events over central India. Nature Communications, 8(1), 1–11.

    Article  Google Scholar 

  • Said, S. E., & Dickey, D. A. (1985). Hypothesis testing in arima (p, 1, q) models. Journal of the American Statistical Association, 80(390), 369–374.

    Article  Google Scholar 

  • Singh, P. (2018). Indian summer monsoon rainfall (ISMR) forecasting using time series data: a fuzzy-entropy-neuro based expert system. Geoscience Frontiers, 9(4), 1243–1257.

    Article  Google Scholar 

  • Singh, P. (2018). Rainfall and financial forecasting using fuzzy time series and neural networks based model. International Journal of Machine Learning and Cybernetics, 9(3), 491–506.

    Article  Google Scholar 

  • Singh, P., & Borah, B. (2013). Indian summer monsoon rainfall prediction using artificial neural network. Stochastic Environmental Research and Risk Assessment, 27(7), 1585–1599.

    Article  Google Scholar 

  • Swaminathan, M. (1998). Padma bhusan prof. P. Koteswaram First Memorial Lecture-23rd, March 3–10.

  • R Core Team. (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria.

  • Tiku, M. L., Wong, W. K., Vaughan, D. C., & Bian, G. (2000). Time series models in non-normal situations: Symmetric innovations. Journal of Time Series Analysis, 21(5), 571–596.

    Article  Google Scholar 

  • Zaveri, E., Grogan, D. S., Fisher-Vanden, K., Frolking, S., Lammers, R. B., Wrenn, D. H., & Nicholas, R. E. (2016). Invisible water, visible impact: Groundwater use and Indian agriculture under climate change. Environmental Research Letters, 11(8), 084005.

    Article  Google Scholar 

Download references

Acknowledgements

The financial grant in the form of a fellowship to the first author by the CSIR, India, is thankfully acknowledged.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-from-profit sectors.

Author information

Authors and Affiliations

Authors

Contributions

MS: conceptualization, methodology, coding and analysis, writing-original draft preparation, writing-reviewing, and editing. YDS: supervision, project administration, resources, validation. PN: reviewing and editing.

Corresponding author

Correspondence to Mohammad Shad.

Ethics declarations

Conflict of Interest

The authors declare that they do not have any kind of conflict regarding this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shad, M., Sharma, Y.D. & Narula, P. Forecasting Southwest Indian Monsoon Rainfall Using the Beta Seasonal Autoregressive Moving Average (\(\beta\)SARMA) Model. Pure Appl. Geophys. 180, 405–419 (2023). https://doi.org/10.1007/s00024-022-03217-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00024-022-03217-3

Keywords

Navigation