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主要的基础知识差距，支持创建准确的洪水地图的能力。该提案将推动关于使用高级分析来估计洪水的新知识，从而改变可用于应对和规划洪水的工具。该提案的工作假设是，建筑规模的洪水检测将通过现有传感器和高级分析的结合来实现。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响应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。将使用五大湖地区的高密度传感器网络对该方法进行验证。该项目的技术成果将产生前所未有的见解和测量的不确定性相关的洪水估计在城市规模。由此产生的实时洪水地图将使雨水管理人员能够领先于居民的投诉，同时通过将工作人员派往最重要的地点来拯救生命和财产。改进后的洪水分布图还将使雨水管理者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高级分析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限度地提高长期基础设施投资。该提案的关键创新是数据方法，将原始的空间分布传感器数据转换为可操作的实时洪水图。这种数据驱动的技术将使同类工具中的第一个能够检测单个建筑物规模的洪水，而不需要在每个位置安装传感器。该奖项反映了NSF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.