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Abstract Reliable flood vulnerability assessment is critical for risk mitigation, yet mainland China faces a persistent bottleneck of historical depth-damage data scarcity. This study proposes an innovative methodology leveraging Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) to develop flood vulnerability curves for buildings in China.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
