Statistical Modelling and Projection of Future Rainfall using SARIMA and Hybrid SARIMA-GARCH Models in Various Zones of Kerala
133 / 111
Keywords:
ARCH-LM test; Heteroscedasticity; Rainfall; Residual; SARIMA; SARIMA-GARCH.Abstract
Water is an important natural resource considered as basic need for all living things around the world. The volume of pure water present in the Earth is regulated by the amount of rainfall received over the years. Sudden climatic changes are observed in throughout the world which led to flood, drought and uneven rainfall over the years. In this study, SARIMA and SARIMA-GARCH models are applied for forecasting rainfall in different zones of Kerala. The presence of heteroscedasticity in residuals obtained from SARIMA model was identified using ARCH-LM test and it was eliminated by applying SARIMA-GARCH model to the same. The ARCH-LM test results confirmed the presence of heteroscedasticity in residuals. The comparison of models used for predicting rainfall revealed that hybrid SARIMA-GARCH model is more efficient in projecting future values of rainfall in the northern and southern zones of Kerala whereas SARIMA model is showing more accuracy in the central zone of Kerala even in the presence of heteroscedasticity of residuals. The comparison of rainfall forecasted in different zones of Kerala clearly indicated that rainfall is higher in the northern zone whereas lower in the southern zone. In the northern and central zones, the rainfall showed a peak from June to September and almost negligible rainfall from December to February. The outperformed model in each zones of Kerala was applied for projection of future rainfall for next 5 years (2021-2025). Compare to previous years, the rainfall in the northern and central zones is expected to decrease whereas in southern zone of Kerala, rainfall will be almost same.
Downloads
References
Adkins, L.C. (2010). Using gretl for Principles of Econometrics, Version 1.3131. https://learneconometrics.com/gretl/ebook.pdf
Banerjee, A., Dolado, J.J., Galbraith, J.W. and Hendry, D. (1993). Co-integration, error correction, and the econometric analysis of non-stationary data. Oxford University Press. https://global.oup.com/academic/product/co-integration-error-correction-and-the-econometric-analysis-of-non-stationary-data-9780198288107?cc=pl&lang=en&
Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31, 307-327. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf
Box, G.E.P. and Jenkins, G.M. (1976). Time series analysis: forecasting and control. Holden-Day, Boca Raton. https://books.google.co.in/books/about/Time_Series_Analysis.html?id=5BVfnXaq03oC&redir_esc=y
Chaudhuri, S. and Dutta, D. (2014). Mann–Kendall trend of pollutants, temperature and humidity over an urban station of India with forecast verification using different ARIMA models. Environmental Monitoring and Assessment, 186(8), 4719-4742. https://www.researchgate.net/profile/Sutapa-Chaudhuri/publication/261363864.pdf
Cottrell, A. and Lucchetti, R. (2012). Gretl User’s Guide. http://sourceforge.net/projects/gretl/files/manual/gretl-guide.pdf/download
Diebold, F.X. and Mariano, R.S. (2002). Comparing predictive accuracy. Journal of Business & Economic Statistics, 20(1), 134-144. https://www.tandfonline.com/doi/abs/10.1198/073500102753410444
Dimri, T., Ahmad, S. and Sharif, M. (2020). Time series analysis of climate variables using seasonal ARIMA approach. Journal of Earth System Science, 129(1), 1-16. https://www.ias.ac.in/article/fulltext/jess/129/00/0149.pdf
Dotse, S.Q. (2024). Deep learning–based long short-term memory recurrent neural networks for monthly rainfall forecasting in Ghana, West Africa. Theoretical and Applied Climatology, 155(4), 3033-3045. https://ui.adsabs.harvard.edu/abs/2023ThApC.155.3033D/abstract
Engle, R.F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1008. http://www.econ.uiuc.edu/~econ536/Papers/engle82.pdf
Franses, P.H. (1991). Seasonality, non-stationarity and the forecasting of monthly time series. International Journal of Forecasting, 7(2), 199-208. https://doi.org/10.1016/0169-2070(91)90054-Y
Gerretsadikan, A. and Sharma, M.K. (2011). Modeling and forecasting of rainfall data of Mekele for Tigray region (Ethiopia). Statistics and Applications, 9(1-2), 31-53. https://www.ssca.org.in/media/3MKSharma.pdf
Google Earth. https://www.google.com/intl/en_in/earth/
Hyndman, R.J. and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 26(3). https://www.jstatsoft.org/article/view/v027i03
Javari, M. (2017). Assessment of dynamic linear and non-linear models on rainfall variations predicting of Iran. Agricultural Engineering International: CIGR Journal, 19(2), 224-240. https://cigrjournal.org/index.php/Ejounral/article/view/4193
Krishnan, G.K.B., Mehta, V. and Yadav, R.S. (2022). Assessment of future pattern of rainfall in different zones of Kerala using incorporation of SARIMA, ANN and Hybrid SARIMA-ANN models. Economic Affairs, 67(05), 823-832. http://ndpublisher.in/admin/issues/EAv67n5q.pdf
Krishnan, G.K.B., Mehta, V. and Rai, V.N. (2023). Stochastic modelling and forecasting of relative humidity and wind speed for different zones of Kerala. Mausam, 74(04), 1053-1064. https://mausamjournal.imd.gov.in/index.php/MAUSAM/article/view/5603
Krishnan, G.K.B. and Mehta, V. (2024). Comparison study on modelling and prediction of weather parameters combining exponential smoothing and artificial neural network models in different zones of Kerala. Environment and Ecology, 42(3), 1094-1103. https://environmentandecology.com/wp-content/uploads/2024/07/MS25-Comparison-Study-on-Modelling-andPrediction-of.pdf
Kwiatkowski, D., Phillips, P.C., Schmidt, P. and Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y
Lama, A., Singh, K.N., Singh, H., Shekhawat, R., Mishra, P. and Gurung, B. (2022). Forecasting monthly rainfall of Sub-Himalayan region of India using parametric and non-parametric modelling approaches. Modeling Earth Systems and Environment, 8, 837-845. https://www.semanticscholar.org/paper/Forecasting-monthly-rainfall-of-Sub-Himalayan-of-Lama-Singh/77297644698b184293276a51ad31c6fb0120129e
Ljung, G.M. and Box, G.E.P. (1978). On a measure of a lack of fit in time series models. Biometrika, 65, 297-303. https://apps.dtic.mil/sti/pdfs/ADA049397.pdf
Lv, P. and Yue, L. (2011). Short-term wind speed forecasting based on non-stationary time series analysis and ARCH model. Proceedings of the 2011 International Conference on Multimedia Technology, 2549-2553. https://www.researchgate.net/publication/241189798_Shortterm_wind_speed_forecasting_based_on_nonstationary_time_series_analysis_and_ARCH_model
Mishra, P., Fatih, C., Vani, G., Lavrod, J.M., Jain, V., Dubey, A. and Choudhary, A.K. (2021). Modeling and forecasting of meteorological factors using ARCH process under different errors distribution specification. Mausam, 72(2), 301-312. https://mausamjournal.imd.gov.in/index.php/MAUSAM/article/view/618/535
Mixon Jr, J. (2009). GRETL: an econometrics package for teaching and research. Managerial Finance, 36(1), 71-81. https://doi.org/10.1108/03074351011006856
Modarres, R. and Ouarda, T.B.M.J. (2013b). Generalized autoregressive conditional heteroscedasticity modelling of hydrologic time series. Hydrological Processes, 27(22), 3174–3191. https://rezamodarres.iut.ac.ir/sites/rezamodarres.iut.ac.ir/files/u129/pdf
Modarres, R. and Ouarda, T.B. (2013a). Modeling rainfall–runoff relationship using multivariate GARCH model. Journal of Hydrology, 499, 1-18. https://rezamodarres.iut.ac.ir/sites/rezamodarres.iut.ac.ir/files/file_pubwdet/pdf
Murthy, K.N., Saravana, R. and 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. https://d1wqtxts1xzle7.cloudfront.net/87826879/s00703-017-0504-220220621-1-ocm8nt-libre.pdf
Narayanan, P., Basistha, A., Sarkar, S. and Kamna, S. (2013). Trend analysis and ARIMA modelling of pre-monsoon rainfall data for western India. Comptes Rendus Geoscience, 345(1), 22-27. https://pdf.sciencedirectassets.com/272261/1-s2.0-S1631071313X00021/1-s2.0-S1631071312002416/main.pdf
Oktaviani, F. and Setiawan, I. (2021). Forecasting sea surface temperature anomalies using the SARIMA ARCH/GARCH model. Journal of Physics: Conference Series, 1882(1), 012-020. https://iopscience.iop.org/article/10.1088/1742-6596/1882/1/012020/pdf
Pandey, P.K., Tripura, H. and Pandey, V. (2019). Improving prediction accuracy of rainfall time series by Hybrid SARIMA–GARCH modeling. Natural Resources Research, 28(3), 1125-1138. https://www.researchgate.net/publication/329627377_Improving_Prediction_Accuracy_of_Rainfall_Time_Series_By_Hybrid_SARIMA-GARCH_Modeling
Praveen, B., Talukdar, S., Shahfahad, Mahato, S., Mondal, J., Sharma, P., Islam, A.R.M.T. and Rahman, A. (2020). Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Scientific Reports, 10(1), 10342. https://pubmed.ncbi.nlm.nih.gov/32587299/
Rahman, M.R. and Lateh, H. (2017). Climate change in Bangladesh: a spatio-temporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model. Theoretical and Applied Climatology, 128(1-2), 27-41. https://www.researchgate.net/profile/Habibah-Lateh/publication/285589104.pdf
Said, S.E. and Dickey, D.A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71, 599–607. http://www.larrylisblog.net/WebContents/Financial%20Models/ADFTest.pdf
Singh, S., Kumar, D., Vishwakarma, D.K., Kumar, R. and Kushwaha, N.L. (2024). Seasonal rainfall pattern using coupled neural network-wavelet technique of southern Uttarakhand, India. Theoretical and Applied Climatology, 155, 5185-5201. https://www.researchgate.net/publication/362206924_Seasonal_rainfall_pattern_using_coupled_neural_network-wavelet_technique_of_of_southern_Uttarakhand_India
Soltani, S., Modarres, R. and Eslamian, S.S. (2007). The use of time series modeling for the determination of rainfall climates of Iran. International Journal of Climatology, 27(6), 819-829. https://rezamodarres.iut.ac.ir/sites/rezamodarres.iut.ac.ir/files/file_pubwdet/pdf
Sultana, N. and Hasan, M.M. (2015). Forecasting temperature in the coastal area of Bay of Bengal – An application of Box–Jenkins seasonal ARIMA model. Civil and Environmental Research, 7(8), 256-272. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf
Swain, S., Nandi, S. and 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://www.springerprofessional.de/development-of-an-arima-model-for-monthly-rainfall-forecasting-o/16251136
Trapletti, A. and Hornik, K. (2013). tseries: Time Series Analysis and Computational Finance, R package version 0.10-32. https://cran.r-project.org/package=tseries
Wang, W., Van Gelder, P.H.A., Vrijling, J.K. and Ma, J. (2005a). Testing and modelling autoregressive conditional heteroscedasticity of streamflow processes. Nonlinear Processes in Geophysics, 12, 55-6. https://npg.copernicus.org/articles/12/55/2005/npg-12-55-2005.pdf
Wang, W., Van Gelder, P.H.A.J.M. and Vrijling, J.K. (2005b). Trend and stationarity analysis for streamflow processes of rivers in western Europe in the 20th century. In Proceedings: IWA International Conference on Water Economics, Statistics, and Finance (Vol. 810). London: IWA. https://www.researchgate.net/profile/Phajm-Gelder/publication/228636735.pdf