RAPID: Fast Reconstruction of Flood Hydrographs in the Houston Metropolitan Area during Hurricane Harvey Based on Image Processing and In-situ Measurements

RAPID：基于图像处理和现场测量快速重建飓风“哈维”期间休斯顿都会区洪水过程线

• 批准号: 1760582
• 负责人: Navid Jafari
• 金额: $ 6.12万
• 依托单位: Louisiana State University
• 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: 2017-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1760582&HistoricalAwards=false
• 关键词: RAPID; Fast; Reconstruction; Flood; Hydrographs; RAPID; Fast; Reconstruction; Flood; Hydrographs

The ability to construct flood hydrographs in urban areas in real-time during flash floods, hurricanes, and other extreme weather events is difficult because of the low spatial density of water level measurements and the complex interactions of built infrastructure, ground topography, and natural landscape with flowing water. The goal of this RAPID project is to leverage perishable images and video footage from traffic intersection and interstate highway cameras, major news media outlets, and social media along with reference objects/points. Subsequent photo image processing, scaled to the reference objects, will enable development of a more continuous, accurate hydrograph in the Houston metropolitan area. By reconstructing the flood hydrographs at a large number of locations in flooded highways, streets and residential subdivisions, high-resolution, process-based urban inundation modeling from hurricane-generated surge and rainfall will become significantly more accurate. For example, it will facilitate a better understanding of transport of sediments and pollutants in and out of Houston during Hurricane Harvey. Such a model validated by the reconstructed hydrographs will also aid state and local governments in making timely evacuation decisions for low-lying areas to mitigate the impact of similar hurricane-induced hazards.This RAPID project based on reconstruction of flood hydrographs in Houston using image processing and in-situ measurements has significant intellectual merit: (1) The proposed methodology is innovative and creative because it does not employ any traditional stream gages. Instead, it relies on a unique form of existing data employed for a new application. (2) The reconstructed flood hydrographs will significantly improve the understanding of the hydrological processes of this unprecedented flood event caused by the extreme rainfall of Hurricane Harvey. (3) The data will benefit the development of a new flood model for Houston. (4) The developed algorithms and software for processing the traffic image data, which will be available on the NHERI DesignSafe-CI platform, can be readily applied to many other flood-prone urban centers, such as New York City, New Orleans, and Miami.

由于水位测量的空间密度低以及建筑基础设施，地面地形和自然景观与流动水的复杂相互作用，因此很难实时在城市地区实时建造洪水水文图，这是困难的。这个快速项目的目的是利用交通交叉路口和州际公路摄像机，主要新闻媒体媒体以及社交媒体以及参考对象/点的可腐烂的图像和录像带。随后的照片图像处理（缩放到参考对象）将能够在休斯顿都会区开发更连续，准确的水文图。通过在洪水泛滥的高速公路，街道和住宅区的大量位置重建洪水水文图，高分辨率，基于过程的城市淹没模型从飓风生成的激增和降雨量将变得更加准确。例如，这将有助于更好地了解哈维飓风期间在休斯敦进出污染物的运输。通过重建的水文验证验证的这种模型还将帮助州和地方政府对低洼地区及时撤离决策，以减轻类似飓风引起的危害的影响。基于休斯顿洪水水文的快速项目使用图像处理和使用图像处理和静脉内测量值进行了智力优点，因此具有重要的方法：（1）具有创造性的方法，因为它具有启发性的方法：（1）具有创造性的作用。相反，它依赖于用于新应用程序的现有数据的独特形式。 （2）重建的洪水水文图将显着提高人们对这一前所未有的洪水事件的水文过程的理解，这是由于哈维飓风的极端降雨而引起的。 （3）数据将使休斯顿新的洪水模型的开发受益。 （4）可以在Nheri DesignSafe-CI平台上获得的开发算法和软件，可在Neri DesignSafe-CI平台上找到，可以轻松地应用于许多其他容易发生洪水的城市中心，例如纽约市，新奥尔良和迈阿密。