用生成式AI把灾害预警变行动指令，提升应急响应效率
A Generative AI-Driven Reliability Layer for Action-Oriented Disaster Resilience
- 用多源数据构建综合风险指数，结合带安全约束的LLM生成个性化建议
- 模拟与试点显示行动执行率提高，响应延迟降低，用户信任度上升
- 适合应急管理部门、智慧城市研究者及负责任AI应用开发者
随着气候灾害加剧，传统早期预警系统虽能快速发布警报，但常无法引发及时防护行动，导致可避免的损失与不平等。我们提出Climate RADAR（风险感知、动态响应与行动推荐系统），一种基于生成式AI的可靠性层，将灾害沟通从「发出警报」转变为「促成行动」。该系统融合气象、水文、脆弱性与社会数据，构建综合风险指数，并利用嵌入安全约束的大语言模型，在市民、志愿者和市政界面提供个性化建议。通过仿真、用户研究与市政试点评估，结果显示保护性行动执行率提升，响应延迟减少，可用性与信任度增强。通过结合预测分析、行为科学与负责任AI，Climate RADAR推动以人为核心、透明且公平的早期预警体系，为合规型灾害韧性基础设施提供实用路径。
原文摘要 · Abstract (English)
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.
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