Towards Autonomous Environmental Management: Artificial Intelligence for Stormwater, Wastewater, and Urban Water Systems

Abstract

The convergence of artificial intelligence, autonomous systems, multi-agent coordination, and pervasive urban sensing is propelling environmental management toward a new paradigm: autonomous urban water governance in which AI agents sense, decide, act, and learn across the full stormwater–wastewater–potable water continuum with minimal human intervention in routine operations. This article presents a comprehensive, forward-looking review of the trajectory toward autonomous environmental management, examining both the current state of AI deployment across urban water systems and the near-future developments — large-scale multi-agent systems, foundation models for hydrology, autonomous field robotics, and federated learning at continental scale — that will define the next decade of the field. Drawing on 116 peer-reviewed studies, documented operational deployments, and emerging research prototypes published between 2020 and 2025, we evaluate progress across eight autonomy dimensions: perception and situational awareness, predictive intelligence, autonomous decision-making, adaptive control, self-healing infrastructure, collaborative multi-agent coordination, explainable and auditable autonomy, and ethical autonomous governance. Our analysis reveals a consistent pattern: autonomy advances most rapidly in domains where consequences of error are limited and reversible (sensor data interpretation, predictive alerting), and more slowly where consequences are high-stakes and irreversible (structural interventions, potable water quality decisions). We introduce the Autonomy Readiness Index for Water Systems (ARIWS) — a quantitative framework for assessing the readiness of specific water management decisions for increasing levels of AI autonomy — and demonstrate its application across 24 representative decision contexts spanning stormwater, wastewater, and potable water management. The article critically examines the ethical, legal, and governance dimensions of autonomous water management — including algorithmic accountability, the rights of communities to human-mediated decisions affecting their water security, and the conditions under which full AI autonomy is socially legitimate. We conclude that achieving beneficial autonomous environmental management requires not only continued technical advancement but a co-evolution of regulatory frameworks, professional standards, community engagement practices, and international governance architectures that ensure autonomous systems serve the public interest equitably and accountably.