A Dynamic Agent-Based Optimization Model for Green Infrastructure to Address Flooding Risks

Green infrastructure (GI) helps manage localized and riverine floods by reducing stormwater runoff and protecting floodplains. GI practices that reduce flooding risks and enhance infiltration include permeable pavements (PPs), rain gardens, and bioswales, among others. PPs are among the frequently used GIs for stormwater management and mitigation of flood risk because they allow more rainfall to soak into the ground and can be easily used in urban areas (i.e., in parking lots and roadway shoulders). While previous research on PPs focused on their benefits and engineering properties, little-to-no studies were conducted to optimize their maintenance practices. Hence, this paper aims at filling this knowledge gap. First, the maintenance, repair, and replacement process of PPs was modeled. Second, an agent-based simulation was developed to model the maintenance-related considerations of PPs. Third, an optimization module was integrated into the developed agent-based model to determine optimal maintenance strategies. The developed agent-based optimization model was applied to New York City's network of PPs to help the city optimize the maintenance of its GI assets by minimizing the total incurred cost of maintenance operations. The findings indicated that the optimal strategy includes 13 service teams, a replacement policy, and 12 maintenance periods after which replacement is required. This study adds to the body of knowledge by proposing a dynamic agent-based model that could be used to improve the functionality and lifespan of PPs, which would ultimately assist in reducing flooding and help communities implement optimal GI strategies.