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

Stephenson, A, Saunders, K and Tafakori, L 2018, 'The MELBS team winning entry for the EVA2017 competition for spatiotemporal prediction of extreme rainfall using generalized extreme value quantiles', Extremes, vol. 21, pp. 477-484.


Document type: Journal Article
Collection: Journal Articles

Title The MELBS team winning entry for the EVA2017 competition for spatiotemporal prediction of extreme rainfall using generalized extreme value quantiles
Author(s) Stephenson, A
Saunders, K
Tafakori, L
Year 2018
Journal name Extremes
Volume number 21
Start page 477
End page 484
Total pages 8
Publisher Springer
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.
Subject Probability Theory
Statistical Theory
Applied Statistics
Keyword(s) Data mining
Extreme rainfall
Generalized extreme value distribution
Spatiotemporal prediction
DOI - identifier 10.1007/s10687-018-0321-0
Copyright notice © Springer Science+Business Media, LLC, part of Springer Nature 2018
ISSN 1572-915X
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