A1: The Urban Flooding Open Knowledge Network (UF-OKN): Delivering Flood Information to AnyOne, AnyTime, AnyWhere

A1：城市洪水开放知识网络（UF-OKN）：向任何人、任何时间、任何地点传递洪水信息

基本信息

批准号：
2033607
负责人：
Lilit Yeghiazarian
金额：
$ 500万
依托单位：
University of Cincinnati Main Campus
依托单位国家：
美国
项目类别：
Cooperative Agreement
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033607&HistoricalAwards=false
关键词：
A1 Urban Flooding Open Knowledge

项目摘要

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.The broader impact and potential societal benefit of this Convergence Accelerator Phase II project is to minimize economic and human losses from future urban flooding in the United States. Floods impact a series of interconnected urban systems – the Urban Multiplex, that include the power grid and transportation network, surface water and groundwater, sewerage and drinking water systems, inland navigation and dams, all of which are intertwined with the socioeconomic and public health sectors. This project uses a convergent approach to integrate these multiple interconnected systems and merges state-of-the-art practices in hydrological and hydraulic engineering; systems analysis, optimization and control; machine learning, data and computer science; epidemiology; socioeconomics; and transportation and electrical engineering to develop an Urban Flood Open Knowledge Network (OKN), the final deliverable of the project. It will be built with unprecedented engagement between urban domain and flooding experts, practitioners, scientists, and technology specialists. This partnership includes universities, nonprofits, private companies, national labs, federal agencies, states, counties and municipalities across the country. The Urban Flood-OKN will empower decision makers and the general public by providing information on how much flooding may occur from a future event and will also show its cascading impact on natural and engineered infrastructures of an urban area. The convergence research and development team supporting this effort has integrated researchers and methods from across disciplines including civil and environmental engineering, hydrology, geography, computer science, meteorology, public safety, emergency response, and economics. The partners engaged as advisors, potential users, and developers include more than a dozen municipalities and water management districts, federal agencies (NOAA, USDOT, NIST, USGS, EPA, FEMA), a national lab (PNNL), non-profits (Consortium of Ocean Leadership, Woods Hole Oceanographic Institution, Consortium of Universities for the Advancement of Hydrologic Science), for-profit organizations, consortia, and individuals. The real impact of flooding on the Urban Multiplex is currently very difficult to quantify because many of its systems are independently designed and managed. Hence an open knowledge network that captures the interconnectedness of these systems and how they impact each other is critically needed. This project will semantically link the Urban Multiplex, whose subsystems generate data that are currently not interoperable. This will enable meaningful queries on flood-related information relevant to urban sustainability. The Urban Flood-OKN will help increase urban resilience and minimize damage from future urban floods due to changing climate and changing land use patterns. It will allow identification of early-warning signals of critical transitions/shifts of a complex interdependent infrastructure system responding to external pressures, and how shifts will be affected by the structure of the Urban Multiplex and failures propagating across its subsystems. This project also has the potential to bring about a societal transformation in the way practitioners, researchers, and the general public engage with, consume, and act upon information about the potential response of the Urban Multiplex to extreme external pressures. This project will allow internet queries that produce actionable information on what to do during storms and flooding, how to plan long-term, and how these decisions will contribute to urban sustainability and resilience – all based on solid science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF融合加速器支持以团队为基础的，基于团队的多学科努力，这些努力应对国家重要性的挑战，并将在不久的将来为社会带来价值的可交付成果。这种收敛加速器II阶段项目的更广泛的影响和潜在的社会益处是为了使未来在美国的城市洪水中最小化经济和人类损失。洪水影响了一系列相互联系的城市系统 - 包括电网和运输网络，地表水和地下水，污水和饮用水系统，内陆导航和大坝，所有这些都与社会经济和公共卫生部门交织在一起。该项目使用收敛的方法来整合这些多个相互联系的系统，并合并水文和水解工程中的最先进实践。系统分析，优化和控制；机器学习，数据和计算机科学；流行病学；社会经济学；以及开发城市洪水开放知识网络（OKN）的运输和电气工程，该项目的最终交付。它将建立在城市领域与洪水专家，从业者，科学家和技术专家之间的空前参与。该合作伙伴关系包括大学，非营利组织，私人公司，国家实验室，联邦机构，州，县和市政当局。 Urban Flood-OKN将通过提供有关未来活动可能发生多少洪水的信息来赋予决策者和公众的能力，并将显示其对市区自然和工程基础设施的层叠影响。支持这项工作的融合研究与发展团队综合了来自跨学科的研究人员和方法，包括民用和环境工程，水文学，地理，计算机科学，气象学，公共安全，紧急响应和经济学。 The partners engaged as advisors, potential users, and developers include more than a dozen municipalities and water management districts, federal agencies (NOAA, USDOT, NIST, USGS, EPA, FEMA), a national lab (PNNL), non-profits (Consortium of Ocean Leadership, Woods Hole Oceanographic Institution, Consortium of Universities for the Advancement of Hydrologic Science), for-profit organizations,财团和个人。目前，洪水对城市多路复用的真正影响很难量化，因为其许多系统都是独立设计和管理的。因此，非常需要一个开放的知识网络，该网络捕捉了这些系统的相互联系以及它们如何相互影响。该项目将在语义上链接城市多路复用，其子系统生成目前无法互操作的数据。这将使有关与城市可持续性相关的洪水相关信息的有意义的疑问。城市洪水将有助于提高城市韧性，并由于气候变化和土地使用方式变化而造成未来城市地板的破坏。它将允许识别复杂的相互依存的基础设施系统的关键过渡/偏移的早期信号，这些基础设施系统响应外部压力，以及如何受到城市多路复用的结构的影响以及在其子系统中传播的失败。该项目还有可能通过从业人员，研究人员和公众参与，消费和采取有关城市多重反应对极端外部压力的潜在反应的信息的方式进行社会转变。该项目将允许互联网查询，这些查询会产生有关在暴风雨和洪水期间要做什么，如何计划以及这些决策将如何对城市可持续性和韧性做出贡献的可行信息 - 所有这些都基于坚实的科学。这项奖项反映了NSF的法定任务，并通过使用该基金会的智力优点和广泛的影响来评估NSF的法定任务，并被视为珍贵的支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Developing an integrated technology-environment-economics model to simulate food-energy-water systems in Corn Belt watersheds

DOI：
10.1016/j.envsoft.2021.105083
发表时间：
2021-09
期刊：
Environ. Model. Softw.
影响因子：
0
作者：
Shaobin Li;Ximing Cai;Seyed Aryan Emaminejad;Ankita Juneja;S. Niroula;Seojeong Oh;Kevin Wallington;R. Cusick;B. Gramig;Stephen John;G. McIsaac;Vijay Singh
通讯作者：
Shaobin Li;Ximing Cai;Seyed Aryan Emaminejad;Ankita Juneja;S. Niroula;Seojeong Oh;Kevin Wallington;R. Cusick;B. Gramig;Stephen John;G. McIsaac;Vijay Singh

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自动提取 FOL 本体的 OWL 版本