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SBIR Phase I: City-scale flood mapping using real-time sensor data

SBIR Phase I: City-scale flood mapping using real-time sensor data
SBIR 第一阶段:使用实时传感器数据进行城市规模洪水测绘
批准号:
2223128
负责人:
Brandon Wong
金额:
$27.44万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-09-15 至 2024-08-31

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英文摘要
This Small Business Innovation Research (SBIR) Phase I project addresses major fundamental knowledge gaps underpinning the ability to create accurate flood maps. This proposal will advance new knowledge on the use of advanced analytics for the estimation of floods, thus transforming the tools available to respond and plan for flooding. The working hypothesis of this proposal is that building-scale flood detection will be achieved through a combination of existing sensors and advanced analytics. This SBIR project will research and develop data-driven flood maps to support targeted flood response and long-term infrastructure planning. Using advanced analytics, existing sensor data will be spatially distributed to create real-time flood maps. The method will be validated using a highly dense sensor network in the Great Lakes region. The technical results of this project will yield unprecedented insights and measurements of uncertainty related to flood estimation at urban scales. The resulting real-time flood maps will allow stormwater managers to stay ahead of resident complaints, while saving lives and property by sending their crews to the most important locations. Improved flood maps will also allow stormwater managers to maximize the impact of long-term infrastructure investments.The project's goal is to make all communities resilient to floods and climate change. To that end, this proposal will show how advanced analytics, driven by wireless sensing, will transform the ability of first responders to save lives, while helping stormwater managers maximize long-term infrastructure investments. The key innovation of this proposal is a data methodology, which will convert raw, spatially distributed sensor data into actionable, real-time flood maps. This data-driven technique will enable the first of its kind tool to detect floods at the scale of individual buildings, without requiring a sensor at every location.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.
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