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CRISP Type 2: dMIST: Data-driven Management for Interdependent Stormwater and Transportation Systems

CRISP 类型 2：dMIST：相互依赖的雨水和运输系统的数据驱动管理

基本信息

批准号：
1735587
负责人：
Jonathan Goodall
金额：
$ 249.92万
依托单位：
University of Virginia Main Campus
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 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1735587&HistoricalAwards=false
关键词：
CRISP Type dMIST Data driven

项目摘要

The overarching objective of this Critical Resilient Interdependent Infrastructure Systems and Processes (CRISP) research project is to create a novel decision support system denoted dMIST (Data-driven Management for Interdependent Stormwater and Transportation Systems) to improve management of interdependent transportation and stormwater infrastructure systems. dMIST is designed specifically to address the critical problem of recurrent flooding caused by sea level rise and more frequent intense storms. The City of Norfolk, Virginia, a national leader in addressing the sea level rise challenge, will collaborate with the research team and serve as the project testbed. With sea level rise and more frequent intense storms, streets in many cities now flood multiple times per year. Flooding of roadways has cascading impacts to other infrastructure systems that depend on the road network including emergency services. Solving the problem of flooded roadways requires new tools capable of analyzing stormwater, transportation, and other infrastructure as interdependent systems. dMIST will be a recommendation system for assisting municipal decision makers and stakeholders in day-to-day operations to mitigate the short-term impacts of road flooding occurrences. It will also offer decision makers novel ways of testing "what if" scenarios that stretch across interdependent infrastructure systems in order to guide how large investments are used to adapt infrastructure systems to a more resilient futurestate.The core intellectual merit of this research is the advancement of a novel modeling and control framework called Data Predictive Control (DPC) for assisting decision makers in understanding and managing interdependent critical infrastructure systems (ICIs). The research is expected to provide four key novel contributions that are critically needed for management of ICIs using DPC. The research is targeted to result in: (1) new methods for data-driven, control-oriented modeling for real-time operations and control synthesis of interdependent stormwater and transportation networks that will complement the knowledge already encoded in existing infrastructure models and decision-making processes; (2) new hybrid-modeling approaches for long-term planning of infrastructure systems that combine the benefits of data-driven models with physics-based (first principles) models to allow decision-makers to explore "what if" scenarios; (3) new recommendation systems whose interpretive capabilities will be evolved in consultation with decision-makers and stakeholders, with this consultation process being studied as part of the research; and (4) new methods to reduce sensing costs that analyze the confidence of recommendations from hybrid models, and how that confidence changes with hypothetical new sensor investments. The research is intended to have broad impact related to national economic and security interests due to its focus on sea level rise. Sea level rise of an additional foot is estimated to cost our nation $200 billion. Given that a common projection for sea level rise is four feet by the end of the century and the nonlinear relationship between sea level rise and infrastructure costs, the total cost will be much higher. This project is also designed to have an immediate impact on Norfolk, the testbed site. Norfolk, because they are considered to be the second most vulnerable city in the nation to sea level rise impacts, provides an ideal testbed for the research goal of producing generalizable outcomes that can be applied to other cities in order to get ahead of this problem. To this end, a specific aim of this work is to encourage innovation in the growing industry of real-time infrastructure monitoring and control to address the challenges introduced by sea level rise.

这个关键弹性相互依赖基础设施系统和过程（CRISP）研究项目的总体目标是创建一个新的决策支持系统，称为dMIST（相互依赖的雨水和运输系统的数据驱动管理），以改善相互依赖的运输和雨水基础设施系统的管理。dMIST是专门为解决海平面上升和更频繁的强烈风暴造成的经常性洪水的关键问题而设计的。弗吉尼亚州的诺福克市是应对海平面上升挑战的国家领导者，将与研究团队合作，并作为项目的试验台。随着海平面上升和更频繁的强烈风暴，许多城市的街道现在每年多次被洪水淹没。道路被淹对依赖公路网的其他基础设施系统，包括应急服务，产生连锁影响。解决道路被淹的问题需要能够分析雨水、交通和其他基础设施作为相互依赖系统的新工具。dMIST将是一个建议系统，用于协助市政决策者和利益相关者进行日常运营，以减轻道路洪水发生的短期影响。它还将为决策者提供测试“如果”情景的新方法，这些情景延伸到相互依赖的基础设施系统，以指导如何使用大量投资来使基础设施系统适应更具弹性的未来状态。这项研究的核心智力价值是一种称为数据预测控制（DPC）的新型建模和控制框架的进步。帮助决策者理解和管理相互依赖的关键基础设施系统（ICI）。这项研究预计将提供四个关键的新贡献，这是迫切需要使用DPC的ICI管理。该研究的目标是：（1）数据驱动的，面向控制的建模的新方法，用于相互依赖的雨水和交通网络的实时操作和控制合成，这将补充现有基础设施模型和决策过程中已经编码的知识;（2）用于基础设施系统长期规划的新混合建模方法，联合收割机数据驱动模型的优势与基于物理的（3）新的建议系统，其解释能力将在与决策者和利益攸关方协商的情况下逐步发展，这一协商过程将作为研究的一部分加以研究;以及（4）降低传感成本的新方法，该方法分析来自混合模型的建议的置信度，以及置信度如何随着假设的新传感器投资而变化。该研究的目的是对国家经济和安全利益产生广泛影响，因为其重点是海平面上升。据估计，海平面再上升一英尺将使我国损失2000亿美元。考虑到海平面上升的普遍预测是到世纪末上升4英尺，以及海平面上升与基础设施成本之间的非线性关系，总成本将高得多。这个项目也被设计为对诺福克，试验台网站产生直接影响。诺福克，因为他们被认为是第二个最脆弱的城市在全国海平面上升的影响，提供了一个理想的试验平台，为研究目标，产生可推广的结果，可应用于其他城市，以提前解决这个问题。为此，这项工作的一个具体目标是鼓励不断发展的实时基础设施监测和控制行业的创新，以应对海平面上升带来的挑战。

项目成果

期刊论文数量（24）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Cloud-Based Data Storage and Visualization Tool for Smart City IoT: Flood Warning as an Example Application

用于智慧城市物联网的基于云的数据存储和可视化工具：洪水预警作为示例应用

DOI：
10.3390/smartcities6030068
发表时间：
2023
期刊：
Smart Cities
影响因子：
6.4
作者：
Leal Sobral, Victor Ariel;Nelson, Jacob;Asmare, Loza;Mahmood, Abdullah;Mitchell, Glen;Tenkorang, Kwadwo;Todd, Conor;Campbell, Bradford;Goodall, Jonathan L.
通讯作者：
Goodall, Jonathan L.

Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning

DOI：
10.2166/hydro.2020.080
发表时间：
2020-10
期刊：
Journal of Hydroinformatics
影响因子：
2.7
作者：
Benjamin D. Bowes;A. Tavakoli;Cheng Wang;Arsalan Heydarian;Madhur Behl;P. Beling;J. Goodall
通讯作者：
Benjamin D. Bowes;A. Tavakoli;Cheng Wang;Arsalan Heydarian;Madhur Behl;P. Beling;J. Goodall

Exploring real-time control of stormwater systems for mitigating flood risk due to sea level rise

探索雨水系统的实时控制，以减轻海平面上升造成的洪水风险

DOI：
10.1016/j.jhydrol.2020.124571
发表时间：
2020
期刊：
Journal of Hydrology
影响因子：
6.4
作者：
Sadler, Jeffrey M.;Goodall, Jonathan L.;Behl, Madhur;Bowes, Benjamin D.;Morsy, Mohamed M.
通讯作者：
Morsy, Mohamed M.

Leveraging Open Source Software and Parallel Computing for Model Predictive Control Simulation of Urban Drainage Systems Using EPA-SWMM5 and Python

利用开源软件和并行计算，使用 EPA-SWMM5 和 Python 进行城市排水系统的模型预测控制仿真