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Assessment of Rainfall Pattern Using ARIMA Technique of Pachmarhi Region, Madhya Pradesh, India

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Book cover Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1037))

Abstract

Rainfall prediction is a crucial event as large portion in India is depends upon it. Since, agriculture is one of the most important constituent on Indian economy and rainfall has an indirect impact on it. In this paper, an attempt has been made to forecast the rainfall activities in terms of pattern matching data analytics work carried over rain fall time series. The major aspect is to study pattern of rainfall over Pachmarhi region. To forecast rainfall of Pachmarhi region data during the years 2000 to 2017 has been collected and Auto Regressive Integrated Moving Average (ARIMA) method was applied to forecast the rainfall for next five years.

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References

  1. Ab Razak, N.H., Aris, A.Z., Ramli, M.F., Looi, L.J., Juahir, H.: Temporal flood incidence forecasting for Segamat River (Malaysia) using autoregressive integrated moving average modelling. J. Flood Risk Manage. 11, 794–804 (2018)

    Article  Google Scholar 

  2. Bari, S.H., Rahman, M.T., Hussain, M.M., Ray, S.: Forecasting monthly precipitation in Sylhet City using ARIMA model. Civil Environ. Res. 7(1), 69–77 (2015)

    Google Scholar 

  3. Dabral, P.P., Murry, M.Z.: Modelling and forecasting of rainfall time series using SARIMA. Environ. Process. 4(2), 399–419 (2017)

    Article  Google Scholar 

  4. Duangdai, E., Likasiri, C.: Rainfall model investigation and scenario analyses of the effect of government reforestation policy on seasonal rainfalls: a case study from Northern Thailand. Atmos. Res. 185, 1–2 (2017)

    Article  Google Scholar 

  5. Farajzadeh, J., Alizadeh, F.: A hybrid linear-nonlinear approach to predict the monthly rainfall over the Urmia Lake watershed using wavelet-SARIMAX-LSSVM conjugated model. J. Hydroinformatics 20(1), 246–262 (2018)

    Article  Google Scholar 

  6. Graham, A., Mishra, E.P.: Time series analysis model to forecast rainfall for Allahabad region. J. Pharmacognosy Phytochem. 6(5), 1418–1421 (2017)

    Google Scholar 

  7. Jain, G., Mallick, B.: A study of time series models ARIMA and ETS. I.J. Mod. Educ. Comput. Sci. 4, 57–63 (2017)

    Article  Google Scholar 

  8. Kalanker, N.V., Somvanshi, V.K., Pandey, O.P., Agrawal, P.K., Prakash, M.R., Chand, R.: Modelling and prediction of rainfall using artificial neural network and ARIMA techniques. J. Ind. Geophys. Union 10(2), 141–151 (2006)

    Google Scholar 

  9. Kulkarni, G.E., Muley, A.A., Deshmukh, N.K., Bhalchandra, P.U.: Autoregressive integrated moving average time series model for forecasting air pollution in Nanded city, Maharashtra, India. Model. Earth Syst. Environ. 4(1), 1–10 (2018)

    Article  Google Scholar 

  10. Mehdizadeh, S., Behmanesh, J., Khalili, K.: New approaches for estimation of monthly rainfall based on GEP-ARCH and ANN-ARCH hybrid models. Water Resour. Manage. 32(2), 527–545 (2018)

    Article  Google Scholar 

  11. Meher, J., Jha, R.: Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India. Sci. Cold Arid Reg. (SCAR) 5(1), 73–84 (2013)

    Article  Google Scholar 

  12. Naveen, V., Anu, N.: Time series analysis to forecast air quality indices in Thiruvananthapuram District, Kerala, India. Int. J. Eng. Res. Appl. 7(6), 66–84 (2017)

    Google Scholar 

  13. Olatayo, T.O., Taiwo, A.I.: Statistical modelling and prediction of rainfall time series data. Global J. Comput. Sci. Technol. 14(1), 1–9 (2014)

    Google Scholar 

  14. Pazvakawambwa, G.T.: A time-series forecasting model for Windhoek Rainfall, Namibia. pp. 1–11 (2017). https://digitalcommons.andrews.edu/cgi/viewcontent.cgi?article=1146&context=arc

  15. Rojo, J., Rivero, R., Romero-Morte, J., Fernández-González, F., Pérez-Badia, R.: Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing. Int. J. Biometeorol. 61(2), 335–348 (2017)

    Article  Google Scholar 

  16. Sahai, A.K., Soman, M.K., Satyan, V.: All India summer monsoon rainfall prediction using an artificial neural network. Clim. Dyn. 16(4), 291–302 (2000)

    Article  Google Scholar 

  17. Singh, P.: Rainfall and financial forecasting using fuzzy time series and neural networks based model. Int. J. Mach. Learn. Cybern. 9(3), 491–506 (2018)

    Article  Google Scholar 

  18. Uba, E.S., Bakari, H.R.: An application of time series analysis in modeling monthly rainfall data for Maiduguri, North Eastern Nigeria. Math. Theory Model. 5(11), 24–33 (2015)

    Google Scholar 

  19. Wang, K.W., Deng, C., Li, J.P., Zhang, Y.Y., Li, X.Y., Wu, M.C.: Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network. Epidemiol. Infect. 145(6), 1118–1129 (2017)

    Article  Google Scholar 

  20. Wang, Y., Wang, C., Shi, C., Xiao, B.: Short-term cloud coverage prediction using the ARIMA time series model. Remote Sens. Lett. 9(3), 275–284 (2018)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of General and Applied Geography, Dr. H. G. Central University, Sagar, M.P., India

    Papri Karmakar

  2. School of Mathematical Sciences, S.R.T.M. University, Nanded, India

    Aniket A. Muley

  3. School of Computational Science, S.R.T.M. University, Nanded, India

    Govind Kulkarni & Parag U. Bhalchandra

Authors
  1. Papri Karmakar
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  2. Aniket A. Muley
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  3. Govind Kulkarni
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  4. Parag U. Bhalchandra
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Corresponding author

Correspondence to Aniket A. Muley .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Karmakar, P., Muley, A.A., Kulkarni, G., Bhalchandra, P.U. (2019). Assessment of Rainfall Pattern Using ARIMA Technique of Pachmarhi Region, Madhya Pradesh, India. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_42

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  • DOI: https://doi.org/10.1007/978-981-13-9187-3_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9186-6

  • Online ISBN: 978-981-13-9187-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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