Joint propagation of probability and possibility in risk analysis: Towards a formal framework
International Journal of Approximate Reasoning
The Dempster--Shafer calculus for statisticians
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Unifying practical uncertainty representations -- I: Generalized p-boxes
International Journal of Approximate Reasoning
Extending stochastic ordering to belief functions on the real line
Information Sciences: an International Journal
Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
Utilizing belief functions for the estimation of future climate change
International Journal of Approximate Reasoning
Belief functions on real numbers
International Journal of Approximate Reasoning
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Estimation of extreme sea levels for high return periods is of prime importance in hydrological design and flood risk assessment. Common practice consists of inferring design levels from historical observations and assuming the distribution of extreme values to be stationary. However, in recent years, there has been a growing awareness of the necessity to integrate the effects of climate change in environmental analysis. In this paper, we present a methodology based on belief functions to combine statistical judgements with expert evidence in order to predict the future centennial sea level at a particular location, taking into account climate change. Likelihood-based belief functions derived from statistical observations are combined with random intervals encoding expert assessments of the 21st century sea level rise. Monte Carlo simulations allow us to compute belief and plausibility degrees for various hypotheses about the design parameter.