Research on flood early warning system of water conservancy project based on artificial intelligence

Floods, as one of the most common and destructive natural disasters, pose a serious threat to people's lives and property safety, as well as the sustainable development of regional socio-economics. Traditional flood warning systems primarily rely on empirical formulas and physical models, often suffering from issues such as delayed responses, insufficient prediction accuracy, and poor adaptability to multi-source complex data. In recent years, artificial intelligence (AI) technology has demonstrated significant advantages in data processing, pattern recognition, and predictive modeling, offering new avenues for the intelligent upgrading of flood warning systems. This paper focuses on flood warning requirements in water conservancy engineering, systematically investigating the key applications of AI technology in rainfall-runoff modeling, hydrological data fusion, and warning decision-making mechanisms. By constructing a warning framework integrated with deep learning models, the accuracy and response efficiency of flood predictions have been significantly enhanced. Additionally, systematic validation through case studies of typical river basins further confirms the feasibility and practical value of this approach. The research findings provide theoretical support and practical foundations for constructing efficient, intelligent, and dynamically adaptive flood warning systems.