CC BY 4.0Karimanzira, DivasDivasKarimanziraRauschenbach, ThomasThomasRauschenbachHellmund, TobiasTobiasHellmundRitzau, LindaLindaRitzau2025-11-252025-11-252025https://publica.fraunhofer.de/handle/publica/499744https://doi.org/10.24406/publica-655910.3390/a1811071310.24406/publica-6559In light of urbanization, climate change, and the escalation of extreme weather events, flood management is becoming more and more important. Improving community resilience and reducing flood risks require prompt decision-making and effective communication. This study investigates how flood management systems can incorporate Large Language Models (LLMs), especially those that use Retrieval-Augmented Generation (RAG) architectures. We suggest a multimodal framework that uses a Flood Knowledge Graph to aggregate data from various sources, such as social media, hydrological, and meteorological inputs. Although LLMs have the potential to be transformative, we also address important drawbacks like governance issues, hallucination risks, and a lack of physical modeling capabilities. When compared to text-only LLMs, the RAG system significantly improves the reliability of flood-related decision support by reducing factual inconsistency rates by more than 75%. Our suggested architecture includes expert validation and security layers to guarantee dependable, useful results, like flood-constrained evacuation route planning. In areas that are vulnerable to flooding, this strategy seeks to strengthen warning systems, enhance information sharing, and build resilient communities.enlarge language modelsflood forecasting and mappingrisk analysisretrieval augmented generationflood knowledge graphjournal article