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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融合加速器支持以使用为灵感，以团队为基础，多学科的努力，解决国家重要性的挑战，并将在不久的将来产生对社会有价值的可交付成果。“融合加速器”二期项目的更广泛影响和潜在社会效益是将美国未来城市洪水造成的经济和人员损失降至最低。洪水影响了一系列相互关联的城市系统——城市综合体，包括电网和交通网络、地表水和地下水、污水处理和饮用水系统、内河航运和水坝，所有这些都与社会经济和公共卫生部门交织在一起。该项目采用一种融合的方法来整合这些多个相互关联的系统，并融合了水文和水利工程领域的最新实践；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;经济学基础;以及交通和电气工程，以开发城市洪水开放知识网络（OKN），这是该项目的最终成果。它将在城市领域和洪水专家、从业者、科学家和技术专家之间进行前所未有的合作。这种伙伴关系包括大学、非营利组织、私营公司、国家实验室、联邦机构、州、县和全国各地的市政当局。城市洪水okn将通过提供有关未来事件可能发生多少洪水的信息，并将显示其对城市地区自然和工程基础设施的级联影响，从而增强决策者和公众的能力。支持这项工作的融合研究和开发团队整合了来自各个学科的研究人员和方法，包括土木与环境工程、水文学、地理学、计算机科学、气象学、公共安全、应急响应和经济学。作为顾问、潜在用户和开发者的合作伙伴包括十多个市政当局和水资源管理区、联邦机构（NOAA、USDOT、NIST、USGS、EPA、FEMA）、国家实验室（PNNL）、非营利组织（海洋领导联盟、伍兹霍尔海洋研究所、促进水文科学大学联盟）、营利性组织、联盟和个人。洪水对城市综合影院的实际影响目前很难量化，因为它的许多系统都是独立设计和管理的。因此，迫切需要一个开放的知识网络，捕捉这些系统的相互联系以及它们如何相互影响。该项目将在语义上连接城市多路复用，其子系统产生的数据目前无法互操作。这将使人们能够对与城市可持续性有关的与洪水有关的信息进行有意义的查询。城市洪水okn将有助于提高城市的复原力，并最大限度地减少气候变化和土地利用模式变化对未来城市洪水造成的损害。它将允许识别对外部压力作出反应的复杂相互依赖的基础设施系统的关键过渡/转移的早期预警信号，以及转移将如何受到城市多路复用结构和跨子系统传播的故障的影响。这个项目也有可能带来一场社会变革，改变实践者、研究人员和公众参与、消费和行动的方式，改变城市综合体对极端外部压力的潜在反应。该项目将允许互联网查询产生可操作的信息，包括在风暴和洪水期间该做什么，如何进行长期规划，以及这些决策将如何促进城市的可持续性和复原力——所有这些都基于可靠的科学。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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