Author(s): Li Li; Kyung Soo Jun

Linked Author(s): Li Li, Kyung Soo Jun

Keywords: Flood control; Interior Point Optimizer; Machine learning; Namgang Dam; Optimal reservoir operation

Abstract: Reservoir operation serves as a critical non-structural measure for real-time flood management, aimed to minimize downstream flood damage while ensuring dam safety. This study develops and evaluates a machine learning-based reservoir operation model with data integration to enhance flood management capabilities. Optimal reservoir outflows are first determined for historical flood events using the Interior Point Optimizer, an optimization model designed to minimize peak outflows. The optimized hydrographs are compared with observed outflows to assess the benefits of improved operational strategies. Machine learning models are then trained and validated using inflow hydrographs and resulting optimal reservoir storage and release data. Various input configurations are tested, incorporating data integration of lagged observations and forecasted values to evaluate their influence on model accuracy. The study also examines multiple hyper-parameter settings to identify the optimal configuration. The methodology is applied to the Namgang Dam in South Korea, simulating hourly operations during flood events. Results indicate that historical reservoir inflow and storage are the most influential inputs, while adding precipitation (historical or forecasted) and/or forecasted inflows does not improve model performance. The machine learning model with data integration successfully replicates optimized reservoir operations, demonstrating its reliability and efficiency in flood management.

DOI:

Year: 2026