Reinforcement-learning-based optimization of investment policies using a mathematical risk model of flood damage

Abstract

Investigating appropriate investment policy for flood risk reduction has become important considering the increased heavy rain disasters due to climate change. This study proposes using the reinforcement learning to minimize the summation of investment and disaster damage costs. We applied the reinforcement learning for a mathematical risk model of flood damage, which is computed by 100-year projected precipitation changes. From the simulations using the trained results, investment plans suggested by the reinforcement learning successfully reduced long-term total costs by 40 % compared to straightforward investment plans. It has been suggested that investment plans of reinforcement learning tend to invest in disaster prevention prior to the rise of flood risk, so that the disaster damage costs can be reduced.