Exploiting Multi-Objective Reinforcement Learning and Explainable Artificial Intelligence to Navigate Robust Regional Water Supply Investment Pathways

Abstract

Urban water utilities are adopting more advanced dynamic and adaptive infrastructure investment frameworks in the face of hydrologic extremes, accelerating demand, and financial constraints. Evolutionary multi-objective reinforcement learning has enhanced the identification of high-performing infrastructure investment pathways that balance conflicting objectives and remain robust amid these challenges. However, current evaluations of robustness are based on highly aggregated regional metrics that potentially conceal emerging individual robustness conflicts between cooperating utilities, largely failing to effectively demonstrate the path-dependent, state-aware nature of these adaptive investment pathways. This study addresses this nontrivial challenge by contributing the Deeply Uncertain (DU) Pathways Time-varying Regional Assessment of Infrastructure Pathways for the Long- and Short-term (TRAILS) framework. We apply the TRAILS framework on the North Carolina Research Triangle, a challenging six-utility cooperative regional system confronting $1 billion in investments to support the maintenance and expansion of its water infrastructure by 2060. Our results reveal that individual robustness preferences can fundamentally change the dynamics and deeply uncertain drivers of the system. We discover critical periods of robustness conflicts between cooperating actors' infrastructure pathways. Furthermore, we apply explainable artificial intelligence methods to reveal that delayed infrastructure construction and rapid demand growth drive robustness during these critical conflict periods. We utilize Information Theoretic sensitivity analysis to clarify consequential state information-action feedbacks between demand, capacity, and storage on individual utilities' decisions. Overall, the analytics facilitated by the DU Pathways TRAILS framework elucidate how financially significant long-term investments and short-term operational actions shape individual and regional robustness over time.