AI-Powered Disaster Response Systems
Using Multi-Agent LLM Frameworks for Air Quality Analysis During Wildfires
This research demonstrates how multi-agent large language model systems can analyze environmental data and recommend policies during disaster scenarios, specifically the January 2025 Los Angeles wildfires.
- Integrates LLMs with cloud-mapping technology to monitor and assess air quality during wildfire events
- Employs an instructor-worker framework for automated large-scale environmental data analysis
- Generates evidence-based policy recommendations for public health protection
- Creates a scalable approach for real-time disaster response management
The medical significance is substantial: this system provides timely, data-driven health recommendations during environmental crises, potentially reducing respiratory complications and other health impacts in affected populations.