Development of an Agent-Powered Decision Support System for Real-Time Flood Control Consultation at the Xiaolangdi Reservoir

Frequent extreme floods and the limitations of traditional manual consultation—information latency, knowledge fragmentation, and experience-dependent reasoning—create an urgent need for intelligent flood control decision support. To address the complex hydrological conditions and urgent consultation demands involved in flood control decision-making at the Xiaolangdi Reservoir, this study develops an intelligent decision support system that enhances emergency response capabilities through the integration of advanced artificial intelligence technologies. The system integrates a multi-agent architecture with Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) to create a comprehensive consultation platform. It incorporates multiple specialized agents, including data analysis agents, consultation reasoning agents, and consultation querying agents, which collaboratively process real-time hydrological data, historical flood records, and operational constraints through carefully designed prompt chains. The RAG component enables efficient retrieval of relevant historical cases and technical specifications from the knowledge base, while the LLM engine generates contextualized consultation suggestions and operational recommendations. Practical deployment demonstrates that the system significantly improves the timeliness of flood control decision-making by substantially reducing consultation time. The prompt engineering framework, incorporating domain-specific templates and adaptive reasoning mechanisms, ensures that the generated consultation advice complies with reservoir operational standards and safety protocols. By providing a scalable intelligent consultation platform, this research substantially enhances decision-making efficiency and reliability during emergency flood events, promoting digital transformation in flood management practices.