Computers are increasingly used in the simulation of natural
phenomena such as floods. However, these simulations are based on
numerical approximations of equations formalizing our conceptual
understanding of flood flows. Thus, model results are intrinsically
subject to uncertainty and the use of probabilistic approaches
seems more appropriate. Uncertain, probabilistic floodplain maps
are widely used in the scientific domain, but still not
sufficiently exploited to support the development of flood
mitigation strategies. In this thesis the major sources of
uncertainty in flood inundation models are analyzed, resulting in
the generation of probabilistic floodplain maps. The utility of
probabilistic model output is assessed using value of information
and the prospect theory. The use of these maps to support decision
making in terms of floodplain development under flood hazard threat
is demonstrated.
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