Integrating Machine Learning and Optimization with Spatiotemporal Techniques to Develop a Methodology for Assessing Rural Resilience

Research into urban resilience has dwarfed the very limited disaster resilience research in rural settings. Because of their different characteristics, a resilience solution in an urban city may not work in a rural environment. This gap between urban and rural readiness became more apparent after catastrophic events such as Hurricane Michael. Adding complexity are the populations at risk, such as the aging population, the most rapidly growing population segment in the State of Florida, and disproportionally the most adversely affected people from storms. As such, there is a clear need to develop novel methodologies along with improvements to the resilience of existing and future infrastructure that can better fit the distinct needs of these rural communities and underserved populations. Moreover, the performance of physical infrastructure systems – whether intact or damaged – is a function of their interaction with social systems. Therefore, there is a need to identify this interaction to fully comprehend the impacts, coping strategies, and barriers to recovery of hurricane victims, particularly differential effects on vulnerable groups such as low-income households, minorities, outdoor workers, the elderly, and the chronically ill. With a focus on Florida’s Panhandle as a test bed, the objective of this project is to develop a methodology to assess the resilience of rural communities against natural disasters such as hurricanes by integrating Geographical Information Systems-based spatiotemporal analysis with machine learning and optimization techniques. Developing the proposed integrated methodology will extend knowledge of community-scale limitations of rural areas in planning for catastrophic storms and provide critical insights into the risks and constraints associated with them. The research team will carefully test and validate the methodology using the Panhandle rural communities and identify the barriers and limitations in the context of data availability, model accuracy, and scalability. The team will leverage the research findings to inform governments and communities towards developing strategic adaptation and implementation plans and to articulate efficient strategies in utilizing these findings in the preparedness, response, and mitigation operations.

Language

• English

Project

• Status: Active
• Funding: $Federal: $134,274 Matching: $67,935
• Contract Numbers:

  69A3552348321

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Florida A&M University, Tallahassee

  404 Foote/Hilyer
  Tallahassee, FL  United States  32307
• Project Managers:

  Moses, Ren

• Performing Organizations:

  Florida State University, Tallahassee

  217 Westcott Building
  Tallahassee, FL  United States  32306-
• Principal Investigators:

  Erman Ozguven, Eren

  Moses, Ren

• Start Date: 20230601
• Expected Completion Date: 20240531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Climate change adaptation; Disaster resilience; Hurricanes; Machine learning; Rural areas; Spatial analysis; Vulnerable road users
• Geographic Terms: Florida
• Subject Areas: Environment; Planning and Forecasting; Security and Emergencies; Transportation (General);

Filing Info

• Accession Number: 01896749
• Record Type: Research project
• Source Agency: Rural Equitable and Accessible Transportation Center
• Contract Numbers: 69A3552348321
• Files: UTC, RIP
• Created Date: Oct 19 2023 4:49PM