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This paper presents a methodology for assessing spatial and temporal uncertainty associated with flood risk management. Traditional modelling approaches focus on either temporal or spatial variability, but not both. There is a need to understand the dynamic characteristics of flood risk and its spatial variability. The traditional two-dimensional (2D) fuzzy set, with one dimension for the universe of discourse and the other dimension for its membership degree, is not sufficient to handle both, spatial and temporal variation of flood risk. The theoretical foundation of this study is based on the development of a three-dimensional (3D) fuzzy set that includes flood risk variability in space and time. The proposed methodology extends the acceptance level of partial flood damage concept to a 3D representation and allows capturing change of decision makers' preferences in time and space. The main objective of the paper is to present an original methodology for flood risk management that is capable of (a) addressing uncertainty caused by spatial and temporal variability and ambiguity; (b) integrating objective and subjective risks; and (c) assisting flood management decision making based on a more detailed understanding of temporal and spatial variability of risk. Presented methodology is illustrated using the Red River flood of 1997 (Manitoba, Canada) as a case study. [ABSTRACT FROM AUTHOR]

Since the use of pervious concrete pavement structures (PCPSs) is essentially still in the trial stage in Canada, long-term and quantitative pavement condition data are not available. The existing approaches applied to assess pervious concrete pavement structure (PCPS) conditions are ad hoc and suffer from methodological limitations. A fuzzy set technique is proposed herein as an efficient tool for dealing with qualitative and incomplete pavement condition data on distress types, severities, densities, and weighting factors. Using this method, a comprehensive fuzzy condition index was developed based on Ministry of Transportation of Ontario (MTO) methodology and using fuzzy pavement condition data. This fuzzy condition index was converted to a single value that allowed for comparisons of pavement conditions using several ranking techniques. A case study of 24 PCPS sites was utilized to demonstrate how the fuzzy representations of the condition index compared with associated single values. It is shown that this approach can effectively provide extensive condition indices for PCPSs and rank them accordingly, using only limited and imprecise pavement condition data. [ABSTRACT FROM AUTHOR]