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

2026-01-26

AI Summary

This work proposes Climate RADAR, a novel early warning system that integrates behavioral science and responsible artificial intelligence to bridge the gap between hazard alerts and protective public action. Traditional early warning systems often fail to motivate timely and effective responses, exacerbating losses and social inequities. Climate RADAR addresses this limitation by generating a composite risk index that fuses hydro-meteorological models, social vulnerability data, and multi-source information. It then leverages a guardrail-constrained generative large language model to deliver personalized, actionable guidance tailored to the public, volunteers, and municipal personnel. Evaluations through simulation, user studies, and municipal pilot deployments demonstrate that the system significantly increases the adoption of protective actions, reduces response latency, and enhances usability and user trust.

Technology Category

Humans and AI: Human-Aware Planning and Behavior PredictionCognitive Modeling & Cognitive Systems: Simulating Human BehaviorPlanning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

User Modeling, Personalization and Recommendation: Fairness-aware retrieval and rankingResponsible Web: Machine-in-the-loop, human agency and autonomySearch and Retrieval-Augmented AI: Personalized, context-aware and across-device search

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.

Problem

• early warning systems
• protective actions
• disaster resilience
• climate hazards
• action execution

Innovation

• Generative AI
• Early Warning Systems
• Large Language Models
• Disaster Resilience
• Action-Oriented Communication