AI-enabled wildfire risk decision support

Problem

Community wildfire exposure combines geography, environment, development, and social conditions. Our team is investigating how to bring those dimensions into a transparent decision-support framework.

Approach How I thought about it

• I’m helping bring environmental, geographic, social, and infrastructure information into a transparent decision-support framework.
• I evaluated potential interpretable machine-learning approaches while documenting the research architecture.

What I built or did

• I developed county-level spatial data architecture, integrating MTBS fire records with U.S. Census geography.
• I developed reproducible processing and validation workflows and worked on SILVIS WUI integration.

Results

• In progress

  Initial data architecture and integration stages have been validated.

• Planned

  Predictive modeling, geographic and temporal holdout validation, interpretable AI, risk maps, community rankings, uncertainty analysis, and a decision-support prototype.

Tools

• Python
• ArcGIS
• GitHub
• MTBS
• Census data
• SILVIS WUI datasets
• Geospatial analysis