Computational Methods in Supporting Spatial Decision Making - Case Studies on Vulnerability Analysis of Critical Infrastructure and Utilisation of Population Information

Hazards, disasters cause insecurity for people and society. Critical infrastructure plays an important role in supporting society and human life, and also in helping to respond to such disasters. Often, the reason for fatalities and financial loss is the inadequacy of critical infrastructure to withstand the cataclysmic effects of natural disasters and the lack of mitigation strategies and preparedness. If the vulnerable locations of the critical infrastructure can be identified and reinforced in advance, the damage and impact can be significantly reduced. Therefore, vulnerability analysis of critical infrastructure is essential. In this dissertation, we formulate four research questions and solve them with five computational methods namely spatio-temporal modelling, graph theory (centrality measures), multi-criteria decision analysis, fuzzy logic and influence diagrams from different fields of science, in order to support spatial decision-making and the vulnerability analysis of critical infrastructure. Most of the methods use population information as one of the parameters and take uncertainty into consideration in the modelling process. In this dissertation, an object oriented spatio-temporal population model was developed to estimate the number of people inside a risk area at a particular time. Graph theory and a set of centrality measures were used to model critical infrastructure´s topological importance. Multi-criteria decision analysis was used to combine various types of input variables to compute an overall vulnerability map. We further developed a fuzzy multi-criteria decision model to solve the vagueness of classification and the decision-making problems with conflict objectives. Finally, we constructed a graphical representation of spatial decision problems by using an influence diagram with fuzzy logic and spatial analysis in order to model spatial objects dependency and it was used to model tree-related electricity outages. Regarding results; a spatio-temporal population model was implemented by using programming languages. The number of people inside a particular risk area at a certain time can be calculated by using this software model. The model is flexible, because it is knowledge based model and user can update his/her knowledge frequently in order to produce more accurate results. The results of using other computational methods are represented as vulnerability maps and vulnerable locations of critical infrastructure in the case of disaster can easily be identified. The biggest benefit of using multi-criteria decision analysis to combine all the attributes, is to save resources in preparedness planning of possible future disasters

Computational Methods in Supporting Spatial Decision Making - Case Studies on Vulnerability Analysis of Critical Infrastructure and Utilisation of Population Information

Hazards, disasters cause insecurity for people and society. Critical infrastructure plays an important role in supporting society and human life, and also in helping to respond to such disasters. Often, the reason for fatalities and financial loss is the inadequacy of critical infrastructure to withstand the cataclysmic effects of natural disasters and the lack of mitigation strategies and preparedness. If the vulnerable locations of the critical infrastructure can be identified and reinforced in advance, the damage and impact can be significantly reduced. Therefore, vulnerability analysis of critical infrastructure is essential. In this dissertation, we formulate four research questions and solve them with five computational methods namely spatio-temporal modelling, graph theory (centrality measures), multi-criteria decision analysis, fuzzy logic and influence diagrams from different fields of science, in order to support spatial decision-making and the vulnerability analysis of critical infrastructure. Most of the methods use population information as one of the parameters and take uncertainty into consideration in the modelling process. In this dissertation, an object oriented spatio-temporal population model was developed to estimate the number of people inside a risk area at a particular time. Graph theory and a set of centrality measures were used to model critical infrastructure´s topological importance. Multi-criteria decision analysis was used to combine various types of input variables to compute an overall vulnerability map. We further developed a fuzzy multi-criteria decision model to solve the vagueness of classification and the decision-making problems with conflict objectives. Finally, we constructed a graphical representation of spatial decision problems by using an influence diagram with fuzzy logic and spatial analysis in order to model spatial objects dependency and it was used to model tree-related electricity outages. Regarding results; a spatio-temporal population model was implemented by using programming languages. The number of people inside a particular risk area at a certain time can be calculated by using this software model. The model is flexible, because it is knowledge based model and user can update his/her knowledge frequently in order to produce more accurate results. The results of using other computational methods are represented as vulnerability maps and vulnerable locations of critical infrastructure in the case of disaster can easily be identified. The biggest benefit of using multi-criteria decision analysis to combine all the attributes, is to save resources in preparedness planning of possible future disasters