A Generative AI-Driven Reliability Layer for Action-Oriented Disaster Resilience

TL;DR

Climate RADAR leverages generative AI and multi-source data to transform traditional early warning systems into action-oriented, personalized disaster resilience tools, improving response effectiveness and trust.

Contribution

Introduces Climate RADAR, a novel AI-driven reliability layer that integrates diverse data and uses large language models to promote timely, personalized disaster protective actions.

Findings

Higher protective action execution rates

Reduced response latency in disaster scenarios

Enhanced usability and trust among users

Abstract

As climate-related hazards intensify, conventional early warning systems (EWS) disseminate alerts rapidly but often fail to trigger timely protective actions, leading to preventable losses and inequities. We introduce Climate RADAR (Risk-Aware, Dynamic, and Action Recommendation system), a generative AI-based reliability layer that reframes disaster communication from alerts delivered to actions executed. It integrates meteorological, hydrological, vulnerability, and social data into a composite risk index and employs guardrail-embedded large language models (LLMs) to deliver personalized recommendations across citizen, volunteer, and municipal interfaces. Evaluation through simulations, user studies, and a municipal pilot shows improved outcomes, including higher protective action execution, reduced response latency, and increased usability and trust. By combining predictive analytics, behavioral science, and responsible AI, Climate RADAR advances people-centered, transparent, and equitable early warning systems, offering practical pathways toward compliance-ready disaster resilience infrastructures.