Combining Statistical and Expert Evidence within the D-S Framework: Application to Hydrological Return Level Estimation

  • Nadia Ben Abdallah
  • Nassima Mouhous Voyneau
  • Thierry Denœux
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

Estimation of extreme sea levels and waves for high return periods is of prime importance in hydrological design and flood risk assessment. The common practice consists of inferring design levels from the available observations and assuming the distribution of extreme values to be stationary. However, in the recent decades, more concern has been given to the integration of the effect of climate change in environmental analysis. When estimating defense structure design parameters, sea level rise projections provided by experts now have to be combined with historical observations. Due to limited knowledge about the future world and the climate system, and also to the lack of sufficient sea records, uncertainty involved in extrapolating beyond available data and projecting in the future is considerable and should absolutely be accounted for in the estimation of design values.

In this paper, we present a methodology based on evidence theory to represent statistical and expert evidence in the estimation of future extreme sea return level associated to a given return period. We represent the statistical evidence by likelihood-based belief functions [7] and the sea level rise projections provided by two sets of experts by a trapezoidal possibility distribution. A Monte Carlo simulation allows us to combine both belief measures to compute the future return level and a measure of the uncertainty of the estimations.

Keywords

Return Period Flood Risk Generalize Extreme Value Return Level Belief Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nadia Ben Abdallah
    • 1
  • Nassima Mouhous Voyneau
    • 2
  • Thierry Denœux
    • 1
  1. 1.CNRS, UMR 7253 HeudiasycUniversité de Technologie de CompiègneCompiègneFrance
  2. 2.Université de Technologie de CompiègneCompiègneFrance

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