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

这个小企业创新研究（SBIR）第一阶段项目解决了创建精确洪水地图的能力所面临的主要基础知识缺口。该提案将推进使用高级分析来估计洪水的新知识，从而改变可用的应对和规划洪水的工具。该提案的工作假设是，通过结合现有传感器和高级分析，可以实现建筑规模的洪水检测。该SBIR项目将研究和开发数据驱动的洪水地图，以支持有针对性的洪水应对和长期基础设施规划。利用先进的分析技术，现有的传感器数据将在空间上分布，以创建实时洪水地图。该方法将在大湖地区使用一个高度密集的传感器网络进行验证。该项目的技术成果将产生前所未有的见解和测量与城市尺度洪水估算相关的不确定性。由此产生的实时洪水地图将使雨水管理人员能够在居民投诉之前采取行动，同时通过派遣工作人员前往最重要的地点来挽救生命和财产。改进的洪水地图还将使雨水管理人员能够最大限度地发挥长期基础设施投资的影响。该项目的目标是使所有社区都能抵御洪水和气候变化。为此，该提案将展示由无线传感驱动的先进分析技术将如何改变急救人员拯救生命的能力，同时帮助雨水管理人员最大化长期基础设施投资。该方案的关键创新是一种数据方法，它将原始的、空间分布的传感器数据转换为可操作的、实时的洪水地图。这种数据驱动的技术将使同类工具中的第一个能够在单个建筑物的规模上检测洪水，而不需要在每个位置都安装传感器。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。

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.