Robust flood risk management decisions : an information-gap approach
Hine, Daniel John
PublisherUniversity of Newcastle upon Tyne
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It is widely accepted that the process of flood management decision making involves considerable uncertainties. In this thesis an alternative approach to decision making is adopted, namely a robust decision making framework. This approach can be characterised by a switch from the predict-then-act methodology of conventional analysis to an assess-risk-of-policy approach. Information-gap is a quantified approach to robust decision making. It expresses uncertainty as an unbounded family of nested sets centred about the nominal values. This uncertainty model, coupled with a satisfying approach, allows for the direct assessment of the trade-off between levels of performance and robustness against uncertainty. This contrasts with the conventional decision making approach of optimising expected return. This thesis introduces the information-gap robust decision making framework into the field of flood risk management, developing appropriate models for uncertainty in both flood frequency predictions and physically based inundation models. The uncertainty model for hydrological parameters is encompassed within the L-moment regional frequency analysis technique, which is currently recommended for flood frequency estimation in the UK. This analysis is also extended to a method which can allow for heterogeneity within the pooling group. Inundation model uncertainty is modelled using a physically based approach utilising an integral measure of unexplained energy losses. Illustrate examples show how this approach can deliver different preference orderings between options compared to conventional analysis, when assessing the cost benefit of options. Finally a brief discussion on the potential to utilise this technique to specify flood defence freeboard, providing explicit allowances for immunity against uncertainty, is considered. This thesis demonstrates that robust decision making techniques provide a valuable tool for engineers, particularly when faced with a requirement to make major investment decisions in the face of uncertainty.