Like all natural hazards, flooding is a complex and inherently
uncertain phenomenon. Despite advances in developing flood
forecasting models and techniques, the uncertainty in forecasts
remains unavoidable. This uncertainty needs to be acknowledged, and
uncertainty estimation in flood forecasting provides a rational
basis for risk-based criteria. This book presents the development
and applications of various methods based on probablity and fuzzy
set theories for modelling uncertainty in flood forecasting
systems. In particular, it presents a methodology for uncertainty
assessment using disaggregation of time series inputs in the
framework of both the Monte Carlo method and the Fuzzy Extention
Principle. It reports an improvement in the First Order Second
Moment method, using second degree reconstruction, and derives
qualitative scales for the interpretation of qualitative
uncertainty. Application is to flood forecasting models for the
Klodzko catchment in POland and the Loire River in France.
Prospects for the hybrid techniques of uncertainty modelling and
probability-possibility transformations are also explored and
reported.
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