SCC-IRG Track 1: Overcoming Social and Technical Barriers for the Broad Adoption of Smart Stormwater Systems

SCC-IRG 第 1 轨道：克服社会和技术障碍，广泛采用智能雨水系统

• 批准号: 1737432
• 负责人: Branko Kerkez
• 金额: $ 187.24万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737432&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Overcoming; Social

In the age of the self-driving car, what role can autonomous technologies play in improving water systems? Floods are the leading cause of severe weather fatalities across the United States. Furthermore, large quantities of metals, nutrients, and other pollutants are washed off during storm events, making their way via streams and rivers to lakes and costal zones. To contend with these concerns, most communities across the United States maintain dedicated infrastructure (pipes, ponds, basins, wetlands, etc.) to convey and treat water during storm events. Much of this stormwater infrastructure is approaching the end of its design life, which results in more flooding and degraded water quality. Instead of building new and bigger stormwater infrastructure, which is cost prohibitive for many communities, it is possible to use existing infrastructure more effectively. The goal of this proposal is to enable the next generation of smart and connected stormwater systems, which use sensors to anticipate changes in weather and the urban landscape, and adapt their operation using active flow controls (e.g., gates, valves, pumps). This will drastically improve community resilience to floods and water quality. Equipping stormwater systems with low-cost sensors and controllers will provide a cost-effective solution to transform infrastructure from static to adaptive, permitting it to be automated and instantly reconfigured to respond to changing community needs and preferences. This research will address a truly national-scale infrastructure challenge and will lay the foundation upon which to empower and educate communities to adopt smart and autonomous stormwater solutions.The research to enable "smart" stormwater systems will be conducted by a team of engineers, social scientists, computer scientist and environmental experts in tight collaboration with decision makers and citizens across four communities in the United States. The team will close fundamental knowledge gaps to explain (1) to what extent real-time control can improve the hydraulic and water quality performance of individual stormwater sites, (2) how to identify and overcome the barriers that public perception poses to the adoption of smart stormwater systems, and (3) how system-level interoperability can be achieved to guarantee safe and effective performance at the scale of entire communities (100s to 1000s of controlled sites). This will be achieved through three closely coupled scientific objectives, which will include testing of laboratory models of control sites, field-scale water quality studies, the formation of community advisory groups, the analysis of residential surveys in each community, and the stability analysis of system-level control algorithms under various sources of uncertainty. The approach is thus fundamentally motivated around the goal of scalability, as the results will be relevant to many communities across the United States, regardless of their size. By open-sourcing the efforts on Open-Storm.org and other public forums, the project will also support research capacity-building by reducing the overhead required by others to deploy smart and connected stormwater systems.

在自动驾驶汽车时代，自动驾驶技术在改善供水系统方面可以发挥什么作用？洪水是美国各地恶劣天气死亡的主要原因。此外，大量的金属、营养物质和其他污染物在风暴期间被冲走，通过溪流和河流进入湖泊和沿海地区。 为了解决这些问题，美国大多数社区都有专门的基础设施（管道、池塘、盆地、湿地等）。用于在风暴期间输送和处理水。大部分雨水基础设施的设计寿命即将结束，这导致更多的洪水和水质下降。 与其建造新的和更大的雨水基础设施，这对许多社区来说成本过高，不如更有效地利用现有的基础设施。该提案的目标是实现下一代智能互联雨水系统，该系统使用传感器来预测天气和城市景观的变化，并使用主动流量控制（例如，闸门、阀门、泵）。这将大大提高社区对洪水和水质的抵御能力。为雨水系统配备低成本的传感器和控制器将提供一个具有成本效益的解决方案，将基础设施从静态转变为自适应，使其能够自动化并立即重新配置，以应对不断变化的社区需求和偏好。这项研究将解决一个真正的国家规模的基础设施的挑战，并将奠定基础，授权和教育社区采用智能和自主的雨水解决方案，以实现“智能”雨水系统的研究将由一个团队的工程师，社会科学家，计算机科学家和环境专家与决策者和公民密切合作，在美国四个社区。 该团队将填补基本知识空白，以解释（1）实时控制可以在多大程度上改善单个雨水站点的水力和水质性能，（2）如何识别和克服公众感知对采用智能雨水系统构成的障碍，以及（3）如何实现系统级互操作性，以保证整个社区范围内的安全和有效性能（100到1000个受控站点）。这将通过三个密切相关的科学目标来实现，其中包括测试控制点的实验室模型，实地水质研究，成立社区咨询小组，分析每个社区的居民调查，以及在各种不确定性来源下系统级控制算法的稳定性分析。 因此，这种方法的基本动机是围绕可扩展性的目标，因为结果将与美国各地的许多社区有关，无论其规模大小。通过在Open-Storm.org和其他公共论坛上开放资源，该项目还将通过减少其他人部署智能和互联雨水系统所需的开销来支持研究能力建设。

项目成果

Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5

• DOI: 10.1016/j.envsoft.2019.07.009
• 发表时间: 2019-10
• 期刊: Environ. Model. Softw.
• 作者: J. Sadler;J. Goodall;Madhur Behl;Mohamed M. Morsy;T. Culver;Benjamin D. Bowes

Technology in support of nature-based solutions requires understanding everyday experiences

支持基于自然的解决方案的技术需要了解日常经验

• DOI: 10.5751/es-12838-260435
• 发表时间: 2021
• 期刊: Ecology and Society
• 影响因子: 4.1
• 作者: Li, Jiayang;Nassauer, Joan Iverson
• 通讯作者: Nassauer, Joan Iverson

Deep reinforcement learning for the real time control of stormwater systems

• DOI: 10.1016/j.advwatres.2020.103600
• 发表时间: 2020-06
• 期刊: Advances in Water Resources
• 影响因子: 4.7
• 作者: Abhiram Mullapudi;Matthew J. Lewis;C. Gruden;B. Kerkez

Landscape elements affect public perception of nature-based solutions managed by smart systems

景观元素影响公众对智能系统管理的基于自然的解决方案的看法

• DOI: 10.1016/j.landurbplan.2022.104355
• 发表时间: 2022
• 期刊: Landscape and Urban Planning
• 影响因子: 9.1
• 作者: Li, Jiayang;Nassauer, Joan Iverson;Webster, Noah J.
• 通讯作者: Webster, Noah J.

StormReactor: An open-source Python package for the integrated modeling of urban water quality and water balance

StormReactor：用于城市水质和水平衡集成建模的开源Python包

• DOI: 10.1016/j.envsoft.2021.105175
• 发表时间: 2021
• 期刊: Environmental Modelling & Software
• 影响因子: 4.9
• 作者: Mason, Brooke E.;Mullapudi, Abhiram;Kerkez, Branko
• 通讯作者: Kerkez, Branko