Sbir phase I: city-scale flood mapping using real-time sensor data

This National Science Foundation (NSF) Small Business Innovation Research (SBIR) Phase I project grant of $274,391 will fund the development of city-scale flood mapping using real-time sensor data. Awarded on September 15, 2022 with a completion date of August 31, 2023, this funding supports research under the NSF Technology, Innovation, and Partnerships program (CFDA 47.084).

Specifically, the awardee Hyfi LLC will advance new knowledge on the use of advanced analytics to estimate floods at urban scales through a combination of existing sensors and new methodologies. Using spatially distributed sensor data and data-driven techniques, the company will create real-time flood maps at the individual building level without requiring a sensor at every location. The results are intended to transform flood response for first responders and support long-term infrastructure planning by stormwater managers.

Abstract

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.