Sbir phase II: ubiquitous flood forecasting using sensors and analytics

Hyfi LLC received a $1.24M Cooperative Agreement award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) on June 15, 2025, to develop an advanced flood forecasting system through the Small Business Innovation Research (SBIR) Phase II initiative. The project, scheduled for completion by May 31, 2027, will deliver a fully operational, self-learning flood prediction platform that integrates real-time sensor data with advanced analytics to provide actionable flood intelligence for stormwater managers and emergency responders.

The primary deliverable is a comprehensive flood forecasting solution comprising three integrated components: (1) an automated methodology to infer urban drainage connectivity using publicly available geospatial data, (2) a predictive flash flood forecasting model that fuses real-time sensor measurements with hydrologic analytics, and (3) an intuitive web-based decision-support tool for end users. The system will enable localized, 24-hour flood forecasts at the municipal infrastructure scale, leveraging an existing network of flood sensors deployed across U.S. cities for validation and refinement. The technology is designed to enhance flood preparedness and community resilience while creating economic opportunities in the smart urban water management sector.

Abstract

The broader impact of this small business innovation research (SBIR) phase II project is to enhance US flood preparedness with real-time monitoring and forecasting. Flash floods are among the most destructive and costly natural disasters in the united states, causing billions of dollars in damage each year. Using sensors and data, this project will advance knowledge to deliver localized, 24-hour forecasts at the scale of municipal infrastructure, allowing emergency responders to take proactive measures before floods escalate. The resulting technology will help stormwater managers mitigate damage, improve public safety, and allocate resources more effectively. The innovation will also drive economic growth by fostering a new market for smart urban water management solutions, creating jobs in the water sector, and positioning the U.S. As a leader in flood forecasting technology. By improving accessibility to high-quality flood prediction tools, this project will help communities of all sizes to build resilience against severe weather events. This small business innovation research (SBIR) phase II project will investigate a novel flood forecasting system that combines real-time sensor data with advanced analytics to provide actionable insights for stormwater managers. The research will focus on three core objectives: (1) synthesize an automated methodology to infer urban drainage connectivity using publicly available geospatial data, (2) implementing a predictive flash flood forecasting model that fuses real-time sensor measurements with hydrologic analytics, and (3) integrating these forecasts into an intuitive web-based decision-support tool. The project will leverage a network of existing flood sensors in cities across the US to refine and validate the approach, ensuring accuracy and generalizability. The anticipated technical outcome is a fully operational, self-learning flood prediction system that can be deployed in any municipality without requiring extensive local calibration. By bridging the GAP between data collection and actionable decision-making, this research will significantly improve flood response capabilities, reducing damage and saving lives in communities nationwide. 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.- subawards are not planned for this award.