| The development of generative AI models offers a new research paradigm for hydrological sciences. This paper takes DeepSeek-R1 as a representative of generative AI model and, based on the actual needs of flood control activities, explores its application potential, current limitations, and future prospects in intelligent flood prevention reasoning through two typical operational scenarios: the reasoning of rainfall-runoff process and the intelligent and rapid recommendation of flood prevent scheduling schemes. The research results show that the DeepSeek-R1 model can effectively combine mechanistic background knowledge with sample data in the rainfall-runoff process reasoning task, learn the mapping characteristics between watershed rainfall-runoff data and the upstream and downstream river discharge data, and perform reasoning on the runoff process under new scenarios. Regarding the intelligent rapid recommendation of flood control scheduling schemes, DeepSeek-R1 exhibits good capabilities in demand understanding and parsing. It can efficiently integrate multi-source information, analyze complex scheduling needs, quickly identify spatio-temporal mapping relationships among rainfall, hydrological, and condition data, and perform result reasoning and regulation scheme recommendations for new rainfall scenarios. The DeepSeek-R1 model is essentially a probabilistic generative model, as opposed to a physics-based mechanistic model. Its predictive capabilities are grounded in the correlation analysis between data and built-in knowledge, rather than physical equation calculations or big data fitting. Therefore, its effectiveness in specialized fields is significantly correlated with the training process, requiring parameter fine-tuning with professional data to enhance its adaptability in the field of flood prevention. This research findings provide a technical pathway and case reference for exploring the application of generative AI models in intelligent flood prevention reasoning. |