How can the human dimensions of disaster impacts be more accurately captured and represented in the analysis, modeling and simulation of disasters?
The availability and access to the human dimensions of disaster impacts used to support planning and management efforts can be difficult to obtain; create; share; and integrate; given that these data can be rendered in a variety of formats and spatio-temporal scales. Artificial Intelligence (AI) as a tool is uniquely positioned to help gather and interpret such large; dynamic; and complex datasets. The digital capabilities and workflow needs will include a significant array of digital modeling and rendering platforms which will also require a network of technology-capable researchers whose experience covers a variety of software. Further; the range of planning goals and constraints; coupled with uncertainty as to when the actually planned activity will take place; makes the development of a standard operating procedure that complies with local needs through engagement processes another challenge. To address the challenges described above; more informed decisions and better affordances for inter-agency coordination may lower the costs of maintaining and using the coastal infrastructure system; which can contribute to novel understanding and provide innovation in addressing infrastructure challenges. The digital twin platform will allow local residents; planners; and decision-makers to communicate; project; and track the impacts of multiple scenarios and to assess the potential social and economic impacts of various construction; maintenance; and alternatives. This research can then serve as a framework; methodological avenue; and easily replicable workflow for other coastal areas to utilize to make better-informed decisions to combat the impact of hazards and climate change on coastal regions and beyond.
What type of data and supporting research infrastructure would be necessary to enable novel, transdisciplinary approaches to answering these and other human-centered disaster questions?
The data and supporting research infrastructure should involve: (1) fusion and high-performance content delivery of human/social-centered data and infrastructure datasets commonly used to support design and planning simulations; (2) development of models reflecting the ontologies of different agents; and (3) design of an intelligent decision support platform that integrates an array of different modeling perspectives; current and planned activities; as well as analytics and visualization of scenarios of interest. We argue that it is necessary to integrate secondary and generated data across multiple software and hardware programs to create the digital twin. Secondary spatial data to develop the platform are expected to be multi-scale; multi-source; multimodal; and multi-formatted. Further; these data will need to be exported across multiple software programs to develop the living digital twin. We can create a proper workflow to make this process achievable by utilizing known import and export tools and shared file types to complete the data gathering; data cleaning; data assembly; data rendering; and model performance modeling. Through proper software and hardware integration; the platform will enable both ubiquitous networked immersion and virtual human teleportation to any location and scale of the built environment. The capabilities to visualize and update the conditions of communities will afford communities rich data instances to create change in their neighborhoods. Simultaneously; the ability to model climate change-based scenarios and test their impacts on the built environment through the workflows developed offers unprecedented capabilities for altering the projected effects of issues such as sea level rise and extreme weather events.
In what ways can US-Japan collaborations advance these questions in new and important ways?
A multidisciplinary US-Japan collaboration can jointly develop digital twin-based decision support systems; which will allow us to (1) collect; compile and share data on physical; cyber; or social infrastructure; (2) engage communities to disseminate information and facilitate citizen science; and (3) promote a human- and social-centered approach for infrastructure planning and integrated social-environment system dynamics modeling in the context of short-term disasters and long-term climate change. In addition to examining the pressing coastal resilient issues into digital twin infrastructures for communities; we can actively engage the community to support stakeholders' needs and create outcomes that are highly intelligent and resilient. While Human-Centered Data for Resilience is a worldwide issue; there is still a lack of comprehensive research on the strategies; technologies; and policies for increasing resilience across countries. Through US-Japan collaborations; experts from multiple disciplines will work together to understand and solve common resilience problems leading to transformative findings on 1) disaster risk reduction through built environmental and infrastructural alteration; 2) methods in which to use technologies to decrease both the adverse effects of disasters and length of recovery periods; and 3) innovative strategies for mitigating disaster impacts in marginalized and socially vulnerable areas. In this sense; agents will gain better insight into: (1) tradeoffs between their efforts to schedule activities vis-a-vis the efforts of other planners; (2) the joint impacts that such planning efforts may have on stakeholders’ individual goals/objectives; and 3) the assets and capacities involved with current dynamic sensors used in digital twin-based information modeling.