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Related Experiment Videos

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

Minghai Chen1, Zhengheng Pu1, Hexiang Yan1

  • 1College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.

Water Research
|June 13, 2026
PubMed
Summary

This study introduces a data-model coevolution mechanism for autonomous pump scheduling in water distribution networks (WDNs). It enhances adaptive updates, improving efficiency and reliability.

Keywords:
AI agentData-centric AIData-model coevolutionIntelligent schedulingOnline learningWater distribution network

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Area of Science:

  • Environmental Engineering
  • Artificial Intelligence
  • Water Resource Management

Background:

  • Urban water distribution networks (WDNs) face challenges in adapting to fluctuating demands, impacting supply reliability and energy efficiency.
  • Current data-centric AI (DCAI) for pump scheduling is limited by static models and data quality issues, hindering autonomous operation.
  • Existing approaches often fail to continuously adapt to real-world network dynamics, creating a barrier for practical implementation.

Purpose of the Study:

  • To develop a data-model coevolution mechanism enabling DCAI to function as an autonomous scheduling agent for WDNs.
  • To address the limitations of static models and poor data quality in existing DCAI applications for WDN pump scheduling.
  • To create a system capable of continuous online learning and self-evolution for adaptive WDN management.

Main Methods:

  • Proposed a data-model coevolution mechanism with a dynamic data curation system for multi-criteria filtering.
  • Implemented a data-coevolving model update mechanism offering retraining and fine-tuning for continuous online learning.
  • Validated the approach on a real Shanghai WDN over 10-week cycles, incorporating an active learning variant.

Main Results:

  • The agent-based mechanism significantly reduced Mean Absolute Error (MAE) by up to 71.03%.
  • Demonstrated improvements in water supply security and energy efficiency through autonomous scheduling.
  • Achieved greater than 25% MAE reduction with an active learning variant, effectively capturing minority operational patterns.

Conclusions:

  • The proposed data-model coevolution mechanism transforms DCAI into an autonomous scheduling agent for WDNs.
  • This synergistic paradigm enables automated adaptive updates through WDN interaction, leading to superior scheduling strategies.
  • The approach enhances WDN management by ensuring reliable water supply while minimizing energy consumption and carbon emissions.