Practice and prospects of artificial intelligence technology empowering statistical investigation of water conservancy construction investment

Statistics on water conservancy construction investment(hereinafter referred to as WCCI) serve as a critical foundation for the planning, construction, management, and government macro-level decision-making of water conservancy projects in China. The quality of this data directly impacts the efficiency of national water conservancy investment arrangements and the effectiveness of related policies. The integration of artificial intelligence (AI) technology into statistical surveys is poised to drive a new transformation in data-driven water governance. This paper systematically explores the implementation pathways, phased outcomes, challenges, and countermeasures of AI in WCCI statistical surveys, using China’s Water Conservancy Statistical Management System(hereinafter referred to as the system) as a practical case study. The research indicates that the application of AI in data processing, multidimensional analysis, and report generation can effectively enhance work efficiency and data quality. However, AI-enabled WCCI statistics currently face multiple challenges, including data silos, security risks, lack of standardisation, insufficient technological investment, and talent shortages. This paper proposes a four-dimensional collaborative strategy encompassing “technology, institutions, data, and talent”. It recommends building a high-quality data resource database, strengthening key technology research and development, and improving cross-departmental collaboration mechanisms to advance the transformation of water conservancy statistics from a “data repository” to a “think tank”, thereby providing insights for the digitalisation of global water governance.