DeepSeek-R1模型在防汛智能推理中的应用探索研究OA

The development of generative AI models offers a new research paradigm for hydrological sciences.This paper takes DeepSeek-R 1 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-R 1 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-R 1 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 per-form result reasoning and regulation scheme recommendations for new rainfall scenarios.The DeepSeek-R 1 model is essentially a probabilistic generative model,as opposed to a physics-based mechanistic model.Its predictive capabili-ties are grounded in the correlation analysis between data and built-in knowledge,rather than physical equation cal-culations or big data fitting.Therefore,its effectiveness in specialized fields is significantly correlated with the train-ing process,requiring parameter fine-tuning with professional data to enhance its adaptability in the field of flood pre-vention.This research findings provide a technical pathway and case reference for exploring the application of genera-tive AI models in intelligent flood prevention reasoning.