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)

基本信息

批准号：
2035685
负责人：
Vidya Samadi
金额：
$ 3.04万
依托单位：
Clemson University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-16 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035685&HistoricalAwards=false
关键词：
RAPID Reconstruction Hurricane Florence Flood

项目摘要

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.

在南卡罗来纳州佛罗伦萨飓风过后，这项研究旨在收集被淹没/损坏的关键基础设施的高水位线 (HWM) 数据，以及来自交通摄像头和社交媒体的易腐烂的图像和视频片段。然后，调查人员将使用数据分析算法和 HWM 数据重建飓风佛罗伦萨洪水过程线 (HF2Hs)，以估计洪水高程和漫溢道路和桥梁的淹没范围。该 RAPID 项目以南卡罗来纳州东部 (SC) 为案例研究，将解决以下问题：关键基础设施上重建的洪水过程线是否可以提供有关洪水阈值和频率的宝贵见解？如果是这样，怎么办？为了解决这些问题，该团队由具有工程水文学、计算机科学和工程学专业知识的成员组成，他们负责提供易腐烂数据集所需的收集、检查和归档服务。收集易腐烂数据的方法融合了通过使用传统（卷尺、工程师尺等）和数据分析技术来增强易腐烂数据收集的更广泛目标，这两种技术都依赖于数据的及时收集。重建的淹没路线/道路和桥梁的洪水过程线将有助于了解关键基础设施如何应对飓风引发的洪水，这种洪水在全球许多地区构成了持续存在的广泛挑战。收集到的数据将有利于开发新的洪水预测数值模型，该模型将处理美国东南部流域的独特需求和概念（浅层含水层参数化）。数据分析算法的目标是灵活且可扩展，以收集和分析大量数据，这些数据将通过开源公共存储库（例如 GitHub）传播。数据的收集和整合旨在促进决策者和以技术为重点的机构之间的沟通/协作。该项目旨在对南卡罗来纳州产生直接影响，该州非常容易遭受反复的飓风事件，并面临日益严重的洪水威胁。该奖项反映了 NSF 的法定使命，并通过使用基金会的评估进行评估，认为值得支持。智力价值和更广泛的影响审查标准。