Prospects and challenges of AIGC technology in hydraulic engineering design

Abstract

Artificial Intelligence Generated Content(AIGC) technology, as an emerging field combining artificial intelligence with generative computation, is rapidly evolving through method such as machine learning, deep neural networks, and generative models. It extracts elements from massive datasets and generates complex content, bringing disruptive transformations to numerous sectors. In the domain of hydraulic engineering design, AIGC technology demonstrates extensive potential applications. These include high-precision river flow predictions based on Long Short-Term Memory(LSTM) neural networks, intelligent water resource management and integrated basin governance using machine learning algorithms, and enabling the entire lifecycle design of hydraulic projects through intelligent systems. However, the unique nature of engineering projects, algorithm applicability, data quality, infrastructure development, talent cultivation, and information security also pose significant challenges to the application of AIGC technology in hydraulic engineering design. Continuous interdisciplinary technological innovation, industry data accumulation, and professional talent development are crucial in addressing these challenges, facilitating the transformative role of AIGC technology in promoting intelligent, refined, and sustainable development in hydraulic engineering design.

Keywords

AIGC technology / hydraulic engineering / digitalization / innovative development