Towards autonomous scheduling agents for water distribution networks: Self-evolving data-centric AI via continuous...

Urban water distribution networks (WDNs) require adaptive scheduling strategies to handle dynamic demand fluctuations. Scientific pump scheduling ensures reliable water supply while minimizing energy consumption and carbon emissions. Data-centric AI (DCAI) enables real-time intelligent scheduling of large-scale WDNs by learning human expertise from historical data, yet its effectiveness is often constrained by data quality and static model deployment. Most existing studies train models once for long-term use, neglecting continuous online updates and limiting practical adaptability; this constitutes a critical barrier for autonomous scheduling agents. To address this issue, this study proposes a data-model coevolution mechanism that transforms DCAI into an autonomous scheduling agent with two core capabilities: (1) a dynamic data curation system that ensures high-quality data streams through multi-criteria filtering, and (2) a data-coevolving model update mechanism with retraining or fine-tuning options for continuous online learning and self-evolution. Validated on a real network in Shanghai over 10-week cycles, the agent-based mechanism reduced MAE by up to 71.03% and improved water supply security and energy efficiency. Interpretability analysis confirmed its directional controllability during autonomous updates. Furthermore, when extended with an active learning variant, the proposed approach achieved greater than 25% MAE reduction across scenarios, addressing the inherent challenge faced by transfer and continual learning approaches in capturing minority operational patterns. By endowing DCAI with autonomous online learning capabilities, this work establishes a synergistic paradigm of intelligent scheduling in which the agent achieves automated adaptive updates through interaction with the WDN, thereby generating superior scheduling strategies.