Digital Twin System for River Network Based on Graph Neural Network

River network management presents a significant challenge in the field of water conservancy, and the subsequent advancements in digital technology offer a promising avenue for addressing this challenge.However, the utilization of cutting-edge technologies like digital twin and deep learning within the water conservancy domain remains largely experimental, hampered by the absence of applicable models, authentic scenarios, and viable solutions.To address this, we propose a river network prediction and response system based on digital twin.This system leverages an improved graph attention network to discern spatial correlations within the detection data of individual sites, integrating bidirectional long short-term memory neural network for predictive analysis.Concurrently, we have developed a suite of dynamic visualization tools designed to streamline real-time situation analysis for users.Through the establishment of an interactive digital twin river network system, we furnish a fresh strategic framework for addressing water conservancy challenges.