Research on an Optimization Model for Water Resource Management Based on Deep Learning

This study proposes HydroCortex, a unified optimization framework for water resource management that explicitly integrates deep learning-based hydrological forecasting with constraint-aware decision-making. It addresses a critical gap in existing methods, which often separate predictive modeling from operational control and rely on static, rule-based policies that underperform in dynamic environments. The central research question investigates how machine learning techniques—such as LSTM-based inflow prediction and graph-structured allocation modules—can support real-time, interpretable, and adaptive water distribution. HydroCortex combines spatiotemporal data processing, scenario-based uncertainty modeling, and multi-agent preference balancing in a modular decision structure. This integration aligns with the growing demand for intelligent, policy-compliant, and data-driven water systems. Experiments across four benchmark datasets and a real-world case in China’s Yellow River Basin show up to 30% improvement in allocation efficiency and notable gains in system robustness. The results demonstrate the model’s scalability, generalizability, and practical relevance under climate variability and diverse stakeholder needs.