A Generative AI-Driven Reliability Layer for Action-Oriented Disaster Resilience
This work addresses the need for more effective and equitable early warning systems for communities facing climate hazards, representing a novel application of AI rather than an incremental improvement.
The paper tackles the problem of conventional early warning systems failing to trigger timely protective actions during climate-related disasters by introducing Climate RADAR, a generative AI-based reliability layer that integrates diverse data and uses guardrail-embedded LLMs to deliver personalized recommendations, resulting in improved outcomes such as higher protective action execution and reduced response latency.
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.