Abstract
Under the dual pressures of global climate change and population growth, water resource risk management faces increasingly severe challenges. The rapid development of big data and artificial intelligence (AI) offers unprecedented water resource risk assessment and response opportunities. This paper systematically explores the applications of big data in water resource management, including multi-source integration of hydrological data, dynamic analysis of supply-demand balance, and optimization of water quality management. With the support of AI models, the predictive capabilities for extreme hydrological events, such as floods and droughts, have significantly improved, playing a critical role, especially in real-time decision-making and emergency response. Additionally, integrating intelligent water resource management systems demonstrates how big data and AI technologies can optimize water resource scheduling, allocation, and long-term planning. However, current technologies still face challenges in data quality, model accuracy, and interdisciplinary collaboration. This paper aims to summarize the latest advancements in big data and AI for water resource risk assessment, analyze existing technical bottlenecks, and propose future research directions to promote intelligent and sustainable water resource management.
