Multi-reservoir flood control using deep reinforcement learning

抄録

With the number of heavy rainfall events on the rise, existing dam facilities must maximize their functions to reduce flood damage. Artificial intelligence (AI) has been applied to improving the efficiency of dam operation during floods, but previous research focused on flood control of a single dam only. In this study, a model to operate multiple dams for flood control was constructed by using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm for deep reinforcement learning. The dam operation AI was applied to operating three dams in the Kinu River basin against generated rainfall data, and the results were compared with flood control according to dam operation rules. The results showed that the dam operation AI could realize a significantly lower average maximum flow in the middle reaches of the Kinu River than that of flood control according to dam operation rules.