山洪灾害预警技术研究进展与智能化发展方向

Research Progress of Flash Flood Early Warning Technologies and Intelligent Development Directions

摘要: 山洪突发性强、预见期短，常与滑坡、泥石流等次生灾害伴生，是山区汛期人员伤亡的主要灾种。本文以判据形式与建模方法为主线，将山洪预警技术划分为阈值预警、水文模型预警、气象水文耦合预警三大类，并围绕判据对象、输入资料、预见期、适用条件与局限五个维度进行逐项比较。结果表明，三类技术在判据与建模方式上差异明显、互为补充，整体呈现出由静态判据向动态判据、由单点信息向多源信息、由经验方法向机理与数据相结合方向演进的趋势。在此基础上，本文进一步讨论了预警技术在降水预报、洪水预报、预警指标与决策支持等环节的智能化发展方向，阐述了人工智能作为赋能方法在各环节的作用及量化成效，剖析了人工智能业务化在数据、模型、算力与人机交互等方面面临的现实挑战。上述研究成果可为山洪灾害监测预警系统的建设与智能化升级提供技术参考，对提升山区防灾减灾能力、保障基层群众生命财产安全具有重要的应用意义。

Abstract: Flash floods are characterized by rapid onset and short lead time, and are often accompanied by secondary hazards such as landslides and debris flows, making them a leading cause of casualties in mountainous areas during flood seasons. Taking the form of warning criteria and the modeling approach as the organizing thread, this paper classifies flash flood early warning technologies into three categories—threshold warning, hydrological model warning, and coupled meteorological-hydrological warning—and compares them across five dimensions: the criterion object, input data, lead time, applicable conditions, and limitations. The results show that the three categories differ markedly yet complement each other in criteria and modeling, collectively evolving from static to dynamic criteria, from single-point to multi-source information, and from empirical methods to mechanism- and data-driven approaches. On this basis, the paper further discusses the intelligent development of early warning in precipitation forecasting, flood forecasting, and warning indicators and decision support, elaborates on the role and quantitative performance of artificial intelligence (AI) as an enabling method across these stages, and examines the practical challenges of operationalizing AI in terms of data, models, computing power, and human-computer interaction. These results can serve as a technical reference for the construction and intelligent upgrading of flash flood monitoring and early warning systems, and are of substantial applied value for strengthening disaster prevention and mitigation capacity in mountainous areas and safeguarding the lives and property of local communities.