A Comparison of Small Area Estimation and Kriging in Estimating Rainfall in Sri Lanka

  • Kalum Udagepola
  • Mithila Perera
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)


Accurate prediction of rainfall is crucial for a country like Sri Lanka as its economy is mainly based on agriculture. Many statistical techniques have been used in the past to estimate rainfall. The main purpose of this study is to evaluate and compare the accuracy of two techniques in estimating seasonal rainfall in Sri Lanka. The two techniques that were compared are namely; Small Area Estimation and Kriging. The four rainfall seasons considered is First Inter Monsoon, South West Monsoon, Second Inter Monsoon and North East Monsoon. Monthly rainfall data collected at 75 rain gauges during the year 2011 were used in this study. Data is withheld from 14 stations were then used to compare the predictions of seasonal rainfall using both techniques. Root Mean Squared Error, Correlation coefficient and scatter plots of observed and fitted seasonal rainfall values were used to compare the two techniques. The Comparison revealed that Kriging provided better predictions for rainfall in First Inter Monsoon and South West Monsoon. Predictions of both techniques were not much successful in estimating rainfall in Second Inter Monsoon. Small area estimation yielded more accurate predictions for rainfall in North East Monsoon.


Small Area Estimation Kriging First Inter Monsoon South West Monsoon Second Inter Monsoon North East Monsoon Root Mean Squared Error Correlation coefficient 


  1. 1.
    Battese, G.E., Harter, R.M., Fuller, W.A.: An error-components model for prediction of county crop areas using survey and satellite data. J. Am. Stat. Assoc. 83(401), 28–36 (1988)CrossRefGoogle Scholar
  2. 2.
    Rao, J.N.K.: Small Area Estimation. Wiley, New York (2003)CrossRefGoogle Scholar
  3. 3.
    Jayawardene, H.K.W.I., Sonnadara, D.U.J., Jayewardene, D.R.: Trends of rainfall in Sri Lanka over the last century. Sri Lankan J. Phys. 6, 7–17 (2005)CrossRefGoogle Scholar
  4. 4.
    Prasad, N.G.N., Rao, J.N.K.: The estimation of mean squared errors of small-area estimators. J. Am. Stat. Assoc. 85, 163–171 (1990)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Stasny, E.A., Goel, P.K., Rumsey, D.J.: County estimates of wheat production. Surv. Methodol. 17(2), 211–225 (1991)Google Scholar
  6. 6.
    Jayasekera, A.S., Wickremasinghe, W.N.: Generating small area statistics for household income in Southern Province of Sri Lanka (2003). Accessed 30 Oct 2016. Doi: Scholar
  7. 7.
    Bosco, A.J.: Small area estimation techniques: focus on under-five mortality data in Uganda. Ph.D. thesis (2014)Google Scholar
  8. 8.
    Punyawardena, B.V.R., Kulasiri, D.: Spatial interpolation of rainfall in the dry zone of Sri Lanka (1999)Google Scholar
  9. 9.
    Jayawardene, H.K.W.I., Sonnadara, D.U.J., Jayewardene, D.R.: Spatial interpolation of weekly rainfall depth in the dry zone of Sri Lanka. Clim. Res. 29(3), 223–231 (2005)CrossRefGoogle Scholar
  10. 10.
    Goovaerts, P.: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J. Hydrol. 228, 113–129 (2000)CrossRefGoogle Scholar
  11. 11.
    Price, D.T., McKenney, D.W., Nalder, I.A., Hutchinson, M.F., Kesteven, J.L.: A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric. For. Meteorol. 101(2), 81–94 (2000)CrossRefGoogle Scholar
  12. 12.
    Di Piazza, A., Conti, F.L., Noto, L.V., Viola, F., La Loggia, G.: Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. Int. J. Appl. Earth Obs. Geoinf. 13(3), 396–408 (2011)CrossRefGoogle Scholar
  13. 13.
    Gomez-Rubio, V., Best, N., Richardson, S. A.: Comparison of different methods for small area estimation. In: ESRC National Centre for Research Methods (2008)Google Scholar
  14. 14.
    Bivand, R.S., Pebesma, E.J., Gómez-Rubio V.: Applied spatial analysis with R. Accessed 30 Oct 2016.
  15. 15.
    Gotway, C.A., Ferguson, R.B., Hergert, G.W., Peterson, T.A.: Comparison of kriging and inverse-distance methods for mapping soil parameters. Soil Sci. Soc. Am. J. 60(4), 1237–1247 (1996)CrossRefGoogle Scholar
  16. 16.
    Introduction to Generalized Linear Mixed models. Accessed 30 Dec 2016.
  17. 17.
    Mukhopadhyay, P.K., McDowell, A.: Small area estimation for survey data analysis using SAS software. In: SAS Global Forum 1–19 (2011)Google Scholar
  18. 18.
    Tobler, W.R.: A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 46(1), 234–240 (1970)CrossRefGoogle Scholar
  19. 19.
    Wagner, P.D., Fiener, P., Wilken, F., Kumar, S., Schneider, K.: Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions. J. Hydrol. 464, 388–400 (2012)CrossRefGoogle Scholar
  20. 20.
    Webster, R., Burgess, T.M.: Optimal interpolation and isarithmic mapping of soil properties III changing drift and universal kriging. J. Soil Sci. 31(3), 505–524 (1980)CrossRefGoogle Scholar
  21. 21.
    Zimmerman, D.L., Stein, M.: Classical geostatistical methods. In: Handbook of Spatial Statistics, pp. 29–44 (2010)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Scientific Research Development Institute of Technology AustraliaLoganleaAustralia
  2. 2.KPSoftRatmalanaSri Lanka

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