Spatial and Temporal Risk Assessment for Water Resources Decision Making

Water resources systems are vulnerable to natural disasters such as floods, wind storms, earthquakes, and various meteorological events. Flooding is the most frequent natural hazard that can cause damage to human life and property. A new methodology presented in this thesis is capable of flood risk management by: (a) addressing various uncertainties caused by variability and ambiguity; (b) integrating objective and subjective flood risk; and (c) assisting the flood risk management based on better understanding of spatial and temporal variability of risk. The new methodology is based on the use of fuzzy reliability theory. A new definition of risk is used and described using three performance indices (i) a combined fuzzy reliability-vulnerability, (ii) fuzzy robustness and (iii) fuzzy resiliency. The traditional flood risk management relies on either temporal or spatial variability, but not both. However, there is a need to understand the dynamic characteristics of flood risk and its spatial variability. The two-dimensional (2-D) fuzzy set that relates the universe of discourse and its membership degree, is not sufficient to address both, spatial and temporal, variations of flood risk. The theoretical contribution of this study is based on the development of a three dimensional (3-D) fuzzy set. The spatial and temporal variability of fuzzy performance indices – (i) combined reliability-vulnerability, (ii) robustness, and (iii) resiliency – have been implemented to (i) river flood risk analysis and (ii) urban flood risk analysis. The river flood risk analysis is illustrated using the Red River flood of 1997 (Manitoba, Canada) as a case study. The urban flood risk analysis is illustrated using the residential community of Cedar Hollow (London, Ontario, Canada) as a case study. The final results of the fuzzy flood reliability analysis are presented using maps that show the spatial and temporal variation of reliability-vulnerability, robustness and resiliency indices. Maps of fuzzy reliability indices provide additional decision support for (a) land use planning, (b) selection of appropriate flood mitigation strategies, (c) planning emergency management measures, (d) selecting an appropriate construction technology for flood prone areas, and (e) flood insurance

A methodology for spatial fuzzy reliability analysis

Natural hazard risk assessment requires quantification of uncertainty that is spatially and temporally variable. Spatial variability of risk has been rarely considered in the past research. This paper presents a new methodology to capture the spatial uncertainty as well as the subjectivity associated with the natural hazard risk analysis. The fuzzy set theory has been integrated with the geographic information system (GIS) in the development of the methodology for spatial reliability analysis. Paper explores the spatial extension of three fuzzy reliability indices i.e. (1) combined reliability-vulnerability, (2) robustness, and (3) resiliency. Fuzzy risk and reliability are quantified within a GIS framework and maps showing spatial variability of three fuzzy indices are developed. The proposed methodology has been applied to flood hazard management. It has been found that the application of spatial fuzzy reliability analysis provides additional information to flood managers regarding the spatial variability of flood risk and aids in the development of a sustainable flood management options

A methodology for spatial fuzzy reliability analysis

Natural hazard risk assessment requires quantification of uncertainty that is spatially and temporally variable. Spatial variability of risk has been rarely considered in the past research. This paper presents a new methodology to capture the spatial uncertainty as well as the subjectivity associated with the natural hazard risk analysis. The fuzzy set theory has been integrated with the geographic information system (GIS) in the development of the methodology for spatial reliability analysis. Paper explores the spatial extension of three fuzzy reliability indices i.e. (1) combined reliability-vulnerability, (2) robustness, and (3) resiliency. Fuzzy risk and reliability are quantified within a GIS framework and maps showing spatial variability of three fuzzy indices are developed. The proposed methodology has been applied to flood hazard management. It has been found that the application of spatial fuzzy reliability analysis provides additional information to flood managers regarding the spatial variability of flood risk and aids in the development of a sustainable flood management options

Reproducibility in systems biology modelling

Reproducibility of scientific results is a key element of science and credibility. The lack of reproducibility across many scientific fields has emerged as an important concern. In this piece, we assess mathematical model reproducibility and propose a scorecard for improving reproducibility in this field