Deep Reinforcement Learning for Optimized Reservoir Operation and Flood Risk Mitigation

Effective reservoir operation demands a careful balance between flood risk mitigation, water supply reliability, and operational stability, particularly under evolving hydrological conditions. This study applies deep reinforcement learning (DRL) models—Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Deep Deterministic Policy Gradient (DDPG)—to optimize reservoir operations at the Soyang River Dam, South Korea, using 30 years of daily hydrometeorological data (1993–2022). The DRL framework integrates observed and remotely sensed variables such as precipitation, temperature, and soil moisture to guide adaptive storage decisions. Discharge is computed via mass balance, preserving inflow while optimizing system responses. Performance is evaluated using cumulative reward, action stability, and counts of total capacity and flood control violations. PPO achieved the highest cumulative reward and the most stable actions but incurred six flood control violations; DQN recorded one flood control violation, reflecting larger buffers and strong flood control compliance; DDPG provided smooth, intermediate responses with one violation. No model exceeded the total storage capacity. Analyses show a consistent pattern: retain on the rise, moderate the crest, and release on the recession to keep Flood Risk (FR) < 0. During high-inflow days, DRL optimization outperformed observed operation by increasing storage buffers and typically reducing peak discharge, thereby mitigating flood risk.