Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood information sources, review communication modalities and channels, synthesize the literature on community leaders’ roles in risk communication, and analyze existing technological tools. Our analysis reveals three key challenges: the fragmentation of flood information, information overload that impedes decision-making, and the absence of a unified communication platform to address these issues. We find that AI techniques can organize data and significantly enhance communication effectiveness, particularly when delivered through infographics and social media channels. Based on these findings, we propose FLAI (Flood Language AI), an AI-driven flood communication platform that unifies fragmented flood data sources. FLAI employs knowledge graphs to structure fragmented data sources and utilizes a retrieval-augmented generation (RAG) framework to enable large language models (LLMs) to produce contextualized narratives, including infographics, maps, and cost–benefit analyses. Beyond flood management, FLAI’s framework demonstrates how AI can transform public service data management and institutional AI readiness. By centralizing and organizing information, FLAI can significantly reduce the cognitive burden on community leaders, helping them communicate timely, actionable insights to save lives and build flood resilience.

Flash floods are increasingly frequent and severe, yet standard risk communication messages are often too generic and lack actionable guidance, causing them to be ignored. This research aims to enhance flood risk communication by first, developing a robust flood ontology using a novel semi-automated approach, and second, demonstrating its potential as a semantic foundation for translating complex data into clear, personalized public alerts. We introduce a semi-automated, human-in-the-loop ontology engineering strategy that integrates expert-defined schemas with Large Language Model (LLM)-driven expansion and refinement from authoritative sources. Evaluation results are twofold: (1) Technical metrics confirm our LLM-constructed ontology achieves superior relationship richness and expressiveness compared with existing disaster ontologies. (2) A proof-of-concept case study demonstrates the ontology’s potential by showing how its specific classes and relations (e.g., ‘neededForElderly’ relation linking the class ‘SpecialConsideration’ to ‘ElderlyCommunityMember’) can be used to generate targeted advice like “check on elderly neighbors”, transforming a generic alert into a clear and actionable message. Consequently, this research delivers two key contributions: a replicable and domain-adaptable methodology for semi-automated ontology construction and a practical demonstration of how such an ontology can bridge the critical gap between flood data and public understanding, empowering communities to respond more effectively.