Harnessing AI in Aiding with Emergency Alerts and Warnings

Recognizing the need for more rapid, effective communications for use during intensifying disasters, this research study explores the potential of Artificial Intelligence (AI) to enhance the drafting of Alerts, Warnings, and Notifications (AWNs). The main goal of the research is to investigate whether an AI model can be trained to follow best practices in emergency communication, as outlined by resources like the "Warning Lexicon" and the Federal Emergency Management Agency (FEMA) Integrated Public Alert and Warning System (IPAWS) and Federal Communications Commission (FCC) guidelines, to produce accurate, actionable messages for Emergency Alert Systems (EAS) and Wireless Emergency Alerts (WEA). The methodology centered around prompt training sessions with specific scenarios and a continuous feedback loop to refine the AI model’s capability to generate precise messages while ensuring the system adhered to strict character limits and message requirements. Key findings from the study show that the AI model was successful at quickly crafting messages in a clear, specific, and actionable manner for both WEA and EAS formats. These findings suggest a potential use for AI to assist in crucial emergency management roles, offering scalability, consistency, and enhanced real-time communication capabilities. This research highlights opportunities for broader integration of AI into emergency management practices, with future research aimed at exploring cross-platform integration, multilingual adaptability, and the long-term effectiveness of AI tools in public safety.