RAPID: Reconstruction of Hurricane Florence Flood Hydrographs (HF2Hs) for South Carolina's Critical Infrastructures Using Data Analytics Algorithms and In-situ Field Measurements

RAPID：使用数据分析算法和现场现场测量重建南卡罗来纳州关键基础设施的飓风弗洛伦斯洪水过程线 (HF2Hs)

• 批准号: 1901646
• 负责人: Vidya Samadi
• 金额: $ 11.5万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901646&HistoricalAwards=false
• 关键词: RAPID; Reconstruction; Hurricane; Florence; Flood

In the wake of Hurricane Florence in South Carolina, this research aims to collect high water marks (HWMs) data across flooded/damaged critical infrastructures, and perishable images and video footage from traffic cameras and social media outlets. The investigators will then reconstruct Hurricane Florence flood hydrographs (HF2Hs) using data analytics algorithms as well as HWMs data to estimate flood elevation and inundation extent over overtopped roads and bridges. Using the eastern portion of South Carolina (SC) as a case study, this RAPID project will address the following questions: Do reconstructed flood hydrographs over critical infrastructures provide valuable insight into flooding thresholds and frequencies? If so, how? To address these questions, the team consists of members with expertise in engineering hydrology and computer sciences and engineering who are positioned to deliver the needed collecting, examining, and archiving of perishable datasets. The methodology for collecting perishable data merges the broader objectives of enhancing perishable data collection through the use of traditional (tape measure, engineer's rule, etc.) and data analytics techniques, both of which depend on the timely collection of data. The reconstructed flood hydrographs for overtopped routes/roads and bridges will help understanding of how critical infrastructures respond to hurricane-induced flooding that presents persistent widespread challenges in many regions worldwide. The collected data will benefit the development of new numerical models for flood prediction that will deal with the unique needs and concepts of the U.S.'s southeast catchments (shallow aquifer parameterization). The data analytics algorithm is targeted be flexible and scalable to collect and analyze large sets of data which will be disseminated through open-source public repositories (e.g., GitHub). The collection and integration of data is targeted to facilitate communication/ collaboration between decision makers and technically-focused institutions. This project is intended to have an immediate impact on South Carolina, a state which is very vulnerable to repeated hurricane events and is under the threat of increasing floods.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.

在南卡罗来纳州的佛罗伦萨飓风之后，该研究旨在收集洪水泛滥/受损的关键基础设施的高水位（HWMS）数据，以及来自交通摄像头和社交媒体渠道的易腐烂图像和录像。然后，研究人员将使用数据分析算法以及HWMS数据重建佛罗伦萨洪水水文图（HF2HS），以估计洪水高程和淹没范围，超过了超过的道路和桥梁。将南卡罗来纳州（SC）的东部作为案例研究，这个快速项目将解决以下问题：重建的关键基础设施上的洪水水文图是否为洪水阈值和频率提供了宝贵的见解？如果是这样，怎么样？为了解决这些问题，该团队由具有工程水文学和计算机科学和工程学专业知识的成员组成，他们可以提供所需的收集，检查和归档可腐烂数据集的成员。收集易腐烂数据的方法合并了通过使用传统（磁带测量，工程师的规则等）和数据分析技术来增强易腐烂数据收集的更广泛目标，这两者都取决于及时收集数据。重建的高层路线/道路和桥梁的洪水水文图将有助于理解关键基础设施如何应对飓风引起的洪水，这在全球许多地区都持续存在广泛的挑战。收集的数据将有利于洪水预测的新数值模型的开发，该模型将处理美国东南流域的独特需求和概念（浅水含水层参数化）。数据分析算法的目标是灵活且可扩展的，以收集和分析大量数据集，这些数据将通过开源公共存储库（例如GitHub）传播。数据的收集和集成旨在促进决策者和以技术为中心的机构之间的沟通/协作。该项目旨在对南卡罗来纳州有直接的影响，南卡罗来纳州非常容易受到反复飓风事件的影响，并且受到增加洪水的威胁。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来评估的。