Advertisement

Extremes

, Volume 21, Issue 3, pp 477–484 | Cite as

The MELBS team winning entry for the EVA2017 competition for spatiotemporal prediction of extreme rainfall using generalized extreme value quantiles

  • Alec G. StephensonEmail author
  • Kate Saunders
  • Laleh Tafakori
Article
  • 121 Downloads

Abstract

We present our winning entry for the EVA2017 challenge on spatiotemporal prediction of extreme precipitation. The aim of the competition is to predict extreme rainfall quantiles that score as low as possible on the competition error metric. Good or bad predictions are defined only by the metric used. Our methodology was simple and produced accurate predictions under this metric. This outcome emphasizes the importance of cross-validation and identifying model over-fitting.

Keywords

Data mining Extreme rainfall Generalized extreme value distribution Spatiotemporal prediction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

We thank the organizing committee of the 10th international conference on Extreme Value Analysis, and Olivier Wintenberger for organizing the prediction challenge. Laleh Tafakori and Kate Saunders would like to thank the Australian Research Council for supporting this work through Laureate Fellowship FL130100039. The authors also acknowledge the support of The Australian Research Council Center of Excellence for Mathematical and Statistical Frontiers.

References

  1. Apputhurai, P., Stephenson, A.G.: Spatiotemporal hierarchical modelling of extreme precipitation in Western Australia using anisotropic Gaussian random fields. Environ. Ecol. Stat. 20, 667–677 (2013)MathSciNetCrossRefGoogle Scholar
  2. Bergmeir, C., Hyndman, R.J., Koo, B.: A note on the validity of cross-validation for evaluating autoregressive time series prediction. Comput. Stat. Data Anal. 120, 70–83 (2018)MathSciNetCrossRefGoogle Scholar
  3. Bücher, A., Segers, J.: On the maximum likelihood estimator for the generalized extreme-value distribution. Extremes 20, 839–872 (2017)MathSciNetCrossRefGoogle Scholar
  4. Coles, S.G.: An Introduction to Statistical Modeling of Extreme Values. Springer, London (2001)CrossRefGoogle Scholar
  5. Coles, S., Pericchi, L.R., Sisson, S.: A fully probabilistic approach to extreme rainfall modeling. J. Hydrol. 273, 35–50 (2003)CrossRefGoogle Scholar
  6. Deidda, R., Puliga, M.: Sensitivity of goodness-of-fit statistics to rainfall data rounding off. Phys. Chem. Earth, Parts A/B/C 31, 1240–1251 (2006)CrossRefGoogle Scholar
  7. Efron, B., Tibshirani, R.: Improvements on cross-validation: the.632 + bootstrap method. J. Am. Stat. Assoc. 92, 548–560 (1997)MathSciNetzbMATHGoogle Scholar
  8. Ferro, C.A., Segers, J.: Inference for clusters of extreme values. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 65, 545–556 (2003)MathSciNetCrossRefGoogle Scholar
  9. Gaetan, C., Grigoletto, M.: A hierarchical model for the analysis of spatial rainfall extremes. J. Agric. Biol. Environ. Stat. 12, 434–449 (2007)MathSciNetCrossRefGoogle Scholar
  10. Gumbel, E.J.: Statistics of Extremes. Columbia University Press, New York (1958)zbMATHGoogle Scholar
  11. Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2016)zbMATHGoogle Scholar
  12. Huser, R., Davison, A.: Space–time modelling of extreme events. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 76, 439–461 (2014)MathSciNetCrossRefGoogle Scholar
  13. Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice. OTexts (2013)Google Scholar
  14. Jenkinson, A.F.: The frequency distribution of the annual maximum (or minimum) of meteorological elements. Q. J. R. Meteorol. Soc. 81, 158–171 (1955)CrossRefGoogle Scholar
  15. Koenker, R.: Quantile Regression. Cambridge University Press, Cambridge (2005)CrossRefGoogle Scholar
  16. Lehmann, E.A., Phatak, A., Stephenson, A.G., Lau, R.: Spatial modelling framework for the characterisation of rainfall extremes at different durations and under climate change. Environmetrics 27, 239–251 (2016)MathSciNetCrossRefGoogle Scholar
  17. Li, Y., Cai, W., Campbell, E.: Statistical modeling of extreme rainfall in southwest western australia. J. Clim. 18, 852–863 (2005)CrossRefGoogle Scholar
  18. Pickands, J. III.: Statistical inference using extreme order statistics. Ann. Stat. 3(1), 119–131 (1975)MathSciNetCrossRefGoogle Scholar
  19. Smith, R.L.: Maximum likelihood estimation in a class of non-regular cases. Biometrika 72, 67–90 (1985)MathSciNetCrossRefGoogle Scholar
  20. Stephenson, A.G., Lehmann, E.A., Phatak, A.: A max-stable process model for rainfall extremes at different accumulation durations. Weather Clim. Extrem. 13, 44–53 (2016)CrossRefGoogle Scholar
  21. Thibaud, E., Mutzner, R., Davison, A.C.: Threshold modeling of extreme spatial rainfall. Water Resour. Res. 49, 4633–4644 (2013)CrossRefGoogle Scholar
  22. Westra, S., Sisson, S.A.: Detection of non-stationarity in precipitation extremes using a max-stable process model. J. Hydrol. 406, 119–128 (2011)CrossRefGoogle Scholar
  23. Wintenberger, O.: Editorial: special issue on the Extreme Value Analysis conference challenge “Prediction of extremal precipitation”. Extremes. To Appear (2018)Google Scholar
  24. Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Cambridge (2017)Google Scholar
  25. Zheng, F., Thibaud, E., Leonard, M., Westra, S.: Assessing the performance of the independence method in modeling spatial extreme rainfall. Water Resour. Res. 51, 7744–7758 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Alec G. Stephenson
    • 1
    Email author
  • Kate Saunders
    • 2
  • Laleh Tafakori
    • 2
  1. 1.Data61CSIROMelbourneAustralia
  2. 2.University of MelbourneMelbourneAustralia

Personalised recommendations