Towards democratized flood risk management: An advanced AI assistant enabled by GPT-4 for enhanced interpretability and public engagement

Traditional flood risk communication fails to bridge the gap between complex technical data and the needs of the public, hindering effective response. This research addresses this gap by developing and validating a novel AI-powered assistant that uses GPT-4 to democratize flood risk information. Our core methodology includes a Retrieval-Augmented Generation (RAG) framework that synthesizes real-time flood warnings, geospatial data, and social vulnerability indices into clear, conversational responses. To validate its effectiveness, we conducted a mixed-methods evaluation, including a comparison across different GPT models. Key quantitative findings reveal that the assistant achieved high performance scores in general flood knowledge (5/5) and handling flash flood alerts (4.3/5). Response times averaged a rapid 12 s for non-function-calling queries, though more complex data retrieval tasks averaged 36 s, highlighting areas for optimization. Our comparison identified GPT-4o as the optimal model for balancing accuracy with response time. The broader implications of this work demonstrate that large language models can serve as powerful tools to translate complex environmental data for non-experts, paving the way for more equitable, engaging, and effective public participation in disaster risk management. • An AI Assistant was developed using GPT-4 to enhance flood risk interpretability and engagement. • Real-time flood warnings, maps, and social vulnerability data were integrated into a framework. • Complex flood information was translated into actionable advice for public and decision-makers. • The Assistant's performance was evaluated on relevance, error resilience, and context accuracy. • Flood risk management was advanced by democratizing access to social and environmental data.