人工智能赋能城市水环境管理的技术路径探讨

Technical Path of Artificial Intelligence Empowering Urban Water Environment Management

鉴于人工智能(Artificial Intelligence,AI)技术在各行业的蓬勃发展以及国内城市对水环境管理日益增长的需求,系统地梳理、探讨了AI在以“监测-预警-溯源-控制”为基本流程的城市水环境管理体系中发挥的作用。基于AI的代理监测技术、监测网络设计以及动态巡检规划,为水环境监测和水设施检测提供了便利;数据驱动的水环境模型相较于传统模型部署快捷高效,能准确把握水质变异的动态;AI支持的专家系统能有效整合利用环境大数据信息,及时确定污染事件;污水的源头管控是个多目标规划问题,通常使用优化算法进行方案研判;AI对污水处理过程和运行环节进行建模,预测并优化污水处理效率和成本;水环境管控作为一项系统工程,按照“厂、网、河、源”一体化的理念采取系统性治理思路,AI在这类复杂系统中的优势比传统方法更加显著,有助于实现对水环境的精细、综合管控。进一步对AI在城市水环境管理领域的发展,包括如何应用环境大数据、整合数据-物理模型、构建新的研究范式和学科阵营以及开展学科教育等一系列未来愿景进行了讨论。最后,从科学、社会学以及哲学角度指出了该领域将要面对的一系列问题,有待后续进一步深入研究。

In view of the rapid development of artificial intelligence(AI)across various industries and the growing demand for water environment management in China,this paper systematically discussed the role of AI in the urban water environment management,focusing on a basic process of monitoringearly warning-tracing-control.AI‑based agent monitoring,monitoring network design and dynamic inspection planning offer enhanced capabilities for water environment monitoring and water facility detection.Compared to traditional models,the data‑driven water environment model can be deployed quickly and efficiently,accurately capturing variations in water quality dynamics.AI‑supported expert systems effectively integrate and utilize large‑scale environmental data to promptly identify pollution events.Source control of sewage presents a multi‑objective planning challenge,commonly addressed using optimization algorithms to evaluate schemes.AI models the sewage treatment process and plant operation,to optimize the efficiency and cost-effectiveness.Water environment management should embrace systematic projects aligned with the concept of plant-network-river-source integration.AI offers significant advantages in such complex systems than traditional methods,which is conducive to the fine and comprehensive management of water environment.This paper explores AI’s development in urban water environment management,including its application of environmental big data,integration of data with physical models,establishment of new research paradigms and disciplines,and advancements in specialized education.Furthermore,it identifies several challenges in this field from scientific,sociological,and philosophical perspectives,urging further exploration by follow‑up researchers.