## 2 Background Knowledge
### Decision Science Support Mechanism and/or Tools  
Decision science has applications in various fields of study and is recognized to provide supportive tools for different types of decision makers to make a concise and unbiased decision [77]. With regards to UR, there are few decision-making studies employing hard data in the post-disaster area, although this is critical to examine observable environmental aspects rather than depending simply on expert opinion. Employing only tacit knowledge is unproductive [78]. Disaster Risk Management (DRM) is a complex process that involves evaluating and mitigating the potential impacts of natural and man-made hazards on communities and infrastructure. Multicriteria Decision Making (MCDM) methods can be useful in DRM by helping decision makers to evaluate and compare alternative options for risk reduction and response [79; 80]. Some of the most widely used MCDM [81] methods in DRM include (i) Analytic Hierarchy Process (AHP): AHP is a method that breaks down a complex decision problem into a hierarchy of smaller, more manageable sub-problems. It is particularly useful in DRM for evaluating and comparing alternative options for risk reduction and response, and for prioritizing response strategies; (ii) Multi-attribute Utility Theory (MAUT): MAUT is a method that allows the decision maker to assign numerical values to each criterion and then combine these values to form a single overall score for each alternative. It is useful in DRM for evaluating and comparing alternative options for risk reduction and response; (iii) Multi-Criteria Decision Analysis (MCDA): MCDA is a generic term that refers to a wide range of methods used to evaluate and compare alternatives based on multiple criteria. It includes methods such as AHP and MAUT, as well as other methods such as the Electre and Promethee methods [81; 82; 83]. In addition to these methods, GIS (Geographic Information System) is also widely used in DRM. GIS can provide spatial context to the data and can be used to display and analyze data in a spatial context, which can help decision makers to understand the problem and evaluate alternatives in a more comprehensive way. GIS can also be used to create hazard and vulnerability maps, which can be used to identify areas that are most at risk and to target risk reduction and response efforts. GIS can also be used to support decision-making by providing real-time information during an emergency response and can be used to analyze the effectiveness of response strategies after a disaster [84]. MCDM methods, such as AHP, MAUT and MCDA, are widely used in disaster risk management to evaluate and compare alternative options for risk reduction and response. GIS is also widely used in DRM as it can provide spatial context to the data and can be used to display and analyze data in a spatial context, support decision-making during an emergency response and can be used to analyze the effectiveness of response strategies after a disaster. With the integration of GIS and MCDM methods, decision makers can have a better understanding of the problem and can evaluate alternatives in a more comprehensive way. The need, potential, and challenges for incorporating Life Cycle Assessment into traditional approaches to decision problems, as well as its application areas on transportation planning, flood management, and food production and consumption, are explored in a study that examines how environmental impacts are taken into account in various fields of interest for decision makers [85]. However, decision support systems alone are not sufficient. These can also benefit from various statistical analysis tools, such as bi-variate correlation, agglomerative hierarchical and non-hierarchical clustering (K-mean), principal component analysis, and multivariate regression models [86]. Urban resilience techniques may be implemented at various stages of the hazard chain, including disaster risk reduction, disaster preparation, and disaster response.