## 5 Discussion
### Knowledge Gaps and Future Study Opportunities on UR and Decision Science  
S.2.1 Resilience Definition and Multidisciplinary Analysis  
Resilience is often characterized as a system's capacity to resist a substantial shock and sustain or promptly continue at normal performance in UR literature. However, there is dispute over both the traits that define resilience and the proper analytical unit for resilience assessment. Because of the many intellectual traditions and lineages represented in the various study fields, there is heterogeneity in how the term of resilience is used [131]. As a result, the context in which it is used may define urban resilience as anything from the capability of the system to adjust to changing environmental conditions to the degree of endurance to maintain functional performance and the ability to sprint back.  
Unified Scalable and Adoptable UR Model  
Predictions appear conceivable over the short-term and in a probabilistic perspective for today's build environment. Even with all the facts in the world, one cannot predict the future; nonetheless, one can establish if systems are prone to cascades or not. Furthermore, faulty system components can be leveraged to provide early warning signals. However, if safety procedures are not taken, spontaneous cascades may become uncontrollable and devastating. To put it another way, predictability and controllability are a result of effective system operation and design. Learning how to put this into effective approaches and how to exploit the good aspects of cascade effects will be a twenty-first-century problem [56].  
There are certain multi-dimensional UR models and frameworks that operate rather well in their intended applications, but by considering the particular needs of different cities and catastrophes, these models must be rebuilt each time by researchers. To that end, a more advanced model that is scalable and adaptive for different disasters and cities based on their demands and priorities is required.  
Geographic Information System (GIS) UR Multidimensional Tools  
There is a requirement to transform all data into geo-tagged transferrable data to enable breaking their information into statistical models and making evaluation by decision support systems possible, in order for high level decision makers to better understand the problem and solution. To improve the model, the GIS-based model should be worked alongside raw data in a cloud-based environment.  
Stochastic Analysis of Virtual Cities  
Because data acquisition is costly and time demanding, extending the acquired data to a broader ecosystem would be extremely valuable. To that aim, if the acquired data do not cover all characteristics of the concept, they can be expanded using inverse distribution employing local or global reverse sampling methods for continuous data and discretevariables dependent on their application. Then, to establish a larger prospective and save survey time and expense, expand this amplified data to all available locations in the city. This solution may not be the most accurate and may be biased in certain circumstances, but it may be used as a tool in research to provide preliminary insight into how to enhance indicators before making final decisions on final dimensions and indicators.