Deep Q-Learning Framework for Quantitative Climate Change Adaptation Policy for Florida Road Network due to Extreme Precipitation

Deep Q-Learning Framework for Quantitative Climate Change Adaptation Policy for Florida Road Network due to Extreme Precipitation

Climate change-induced extreme weather and increasing population are increasing the pressure on the global aging road networks. Adaptation requires designing interventions and alterations to the road networks that consider future dynamics of flooding and increased traffic due to the growing population. This paper introduces a reinforcement learning approach to designing interventions for Florida's road network under future traffic and climate projections. Three climate models and a tide and surge model are used to create flooding and coastal inundation projections, respectively. The optimal sequence of decisions for adapting Florida's road network to minimize flooding-related disruptions is solved by using a graph-based deep neural network that assesses the impact of a set of modifications to the road network for road network connectivity and load balance. Results indicate that coastal towns and inland towns where many highways meet require substantial modifications to their road networks to ensure road network connectivity throughout the state in the next 50 years.