Almost $100 million was spent on recovery efforts for Puerto Rico when Hurricane Maria hit in 2017. Mid-Atlantic hurricanes devastate many locations off the East coast of the United States every year. They are highly unpredictable and cause many deaths and damage that is not recoverable. Therefore, quick and effective relief efforts are imperative. Rapid mobilization of resources and clear distribution processes make a significant difference in providing effective relief. Support for the disaster management cycle – including mitigation, preparation, response, recovery – requires informed decision making with input from many stakeholders and perspectives. Therefore, to prepare and improve these efforts and prevent disarray similar to the occurrence in Puerto Rico in 2017, we leverage Digital Engineering to develop models by which to better inform decision makers on exactly where and when to send relief supplies. To accomplish this modeling, the authors instantiate the Environment-Vulnerability-Decision-Technology (EVDT) Framework developed at the MIT Space Enabled Research Group. This framework links societal, economic, scientific, and technological data to provide a more holistic view of risks associated with natural disasters. Furthermore, this framework makes use of Earth Observation data taken from the growing plethora of information available from satellite-based monitoring systems. The methods presented In this presentation, developed using the EVDT framework and Model Based Systems Engineering language SysML, map out an approach to use Geographical Information System data for Puerto Rico to categorize Vulnerabilities associated with different regions and develop a hierarchical risk assessment for Puerto Rico disaster relief. This work presents the models alongside the results of a literature review on the response to Hurricane Maria to suggest to decision makers the increased use of the electric Vertical Take Off and Lift (eVTOL) vehicle technology and regional air drop missions. This presentation also describes a framework for the development of a SysML profile for the EVDT Framework, enabling its reuse in disaster relief mission design.
Mitchell Kirshner is a PhD Candidate at the University of Arizona studying Systems and Industrial Engineering. His research focuses on MBSE for cyberphysical autonomous space systems and his career interests including advancing human space travel. Mitchell completed his MS degree in Chemical and Biological Engineering at Northwestern University in 2015, where he also graduated with his BS in Earth and Planetary Sciences, the Integrated Science Program, and Integrated Engineering. Mitchell’s career experience spans from engineering nuclear submarines to rendering data science applications in virtual reality. He is originally from Long Island, NY.