深度强化学习控制作为城市排水系统的创新方法:审查和前景
Zichen He1, Wenchong Tian2, Jiaying Wang1
1College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
Water research
|June 15, 2025
概括
深度强化学习控制 (DRLC) 显示出优化城市排水系统 (UDS) 的前景. 需要进一步的研究,以解决现实世界实施的技术差距,并提高UDS的弹性.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 水资源管理 水资源管理
背景情况:
- 城市排水系统 (UDS) 面临着城市化,气候变化和基础设施老化带来的越来越多的挑战.
- 实时控制 (RTC) 通过调整管理策略和重新使用现有系统来改善UDS性能.
- 深度强化学习 (DRL) 是一种强大的人工智能技术,用于复杂,动态环境中的决策.
研究的目的:
- 系统地审查深度强化学习控制 (DRLC) 在城市排水系统 (UDS) 的进展情况.
- 确定阻碍在UDS中广泛采用和实际实施DRLC的关键局限性.
- 提出建设性的见解,以推动未来的研究在DRLC的UDSs.
主要方法:
- 在UDS中对DRLC应用的系统文献综述.
- 分析DRLC在减轻洪水,减少下水道溢出,水质管理和废水处理方面的潜力.
- 识别DRLC实施的技术和实践缺口.
主要成果:
- DRLC显示出优化UDS控制的巨大潜力,包括减少洪水和改善水质.
- 目前的应用程序看起来很有前途,但面临着与数据管理,模型设计和可解释性相关的挑战.
- 审查强调需要强大的基准框架和安全的控制策略.
结论:
- DRLC为UDS控制优化提供了变革性的机会.
- 解决数据,建模,安全和可解释性方面的缺陷对于实际部署DRLC至关重要.
- 未来的研究应该专注于通过先进的DRLC框架来增强UDS的弹性.
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