使用深度强化学习提高城市排水系统的弹性
Wenchong Tian1, Zhiyu Zhang2, Kunlun Xin3
1School of Energy and Environment, City University of Hong Kong, Hong Kong SAR, China; State Key Laboratory of Marine Pollution, City University of Hong Kong, Hong Kong SAR, China.
Water research
|April 24, 2025
概括
新的深度强化学习 (DRL) 模型提高了城市排水系统 (UDS) 的弹性. 通过新型奖励功能进行训练,这些DRL方法可以减少洪水和综合下水道溢出,同时提高系统的稳定性.
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:
- 实时控制 (RTC) 对于管理城市排水系统 (UDS),减轻洪水和综合下水道溢出至关重要.
- 深度增强学习 (DRL) 提供了先进的RTC功能,但往往忽视了系统的弹性.
- 现有的DRL方法主要集中在减少排放和洪水,忽视了UDS弹性的关键方面.
研究的目的:
- 开发新的DRL模型,结合新的奖励功能,以特别提高UDS的弹性.
- 评估这些增强的DRL模型在提高UDS弹性以及传统性能指标的有效性.
- 分析降雨模式和DRL模型类型对系统性能和概括性的影响.
主要方法:
- 开发新的DRL模型,利用两个不同的奖励功能,以优先考虑UDS弹性.
- 这些DRL模型在中国东部城市排水系统的案例研究中实施和测试.
- 在不同降雨条件和不同DRL架构下对模型性能进行比较分析.
主要成果:
- 开发的DRL模型成功地提高了UDS的弹性,同时减少了洪水和综合下水道溢出.
- 模型性能因降雨强度和使用的特定DRL模型而有显著差异.
- 基于价值的DLR模型,训练有持续时间加权奖励函数,在案例研究中表现出优异的表现.
结论:
- 新的DRL奖励功能可以有效地提高UDS的弹性,减少环境影响.
- 降雨特征和DRL模型选择是影响RTC策略成功的关键因素.
- 以价值为基础的DRL方法与按持续时间加权的奖励显示出对弹性城市排水管理的重大承诺.
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