Coupling Knowledge Graphs with Large Language Models for Integrated Runoff Forecasting and Reservoir Operation: A Case Study of the Upper Yellow River

The Upper Yellow River serves as the basin's primary water conservation zone and multi-year regulation reservoir. However, the region exhibits frequent alternations between persistent wet and dry cycles, along with abrupt regime shifts, which significantly amplify the uncertainty and complexity of water resource regulation. Under these complex conditions, traditional hydrological models often suffer from deteriorating forecast accuracy and limited lead times, failing to support precise and adaptive decision-making. To address these challenges, this study proposes a Physics-AI coupled framework that integrates Knowledge Graphs (KG) with Large Language Models (LLMs) to create a closed loop from perception to decision-making. First, a multimodal KG was constructed to standardize heterogeneous data and, more critically, to encode hydrological evolution rules as logical constraints for physical reasoning. Driven by this cognitive foundation, we developed a multi-scale forecasting system: the Parallel LSTM-and-Sequence-GPT (PLSG) for daily-scale medium-term forecasting, and the physics-informed Hydro-LSTM for monthly-scale long-term runoff reconstruction. To bridge the gap between forecasting and operation, accurate runoff inputs are integrated into a Mixture of Experts (MoE) framework. Here, autonomous agents dynamically configure scheduling workflows to execute multi-objective optimization, ensuring adaptability across diverse hydrological scenarios. Validation results show that the PLSG model improved 15-day forecast accuracy by 31.3% against baselines, while Hydro-LSTM achieved a NSE of 0.65–0.857. This framework not only enhances forecast resilience but also enables autonomous multi-objective optimization with transparent decision-making pathways, providing a robust and interpretable tool for complex water system management.