I-Corps: A machine learning and video-based sensor for measuring sewer flows

Award ID: 2101934

The broader impact/commercial potential of this I-Corps project is in the improvement of water reclamation operations and associated positive impacts on human and environmental health. Water reclamation facilities in the U.S. discharge billions of gallons of untreated wastewater into the environment each year due to large storms that cause overflows in their collection systems. Failures in these systems can also result in basement backups of untreated water into citizens' homes, which can cause property damage and risk of serious illnesses. To overcome these challenges, water reclamation facilities rely on sewer flow data to get a picture of what is happening within their pipe networks; however, existing sensors have several shortcomings including inaccurate data, high costs, and an inability to detect low flows. The proposed technology seeks to overcome these challenges through a machine learning and video-based sensor for measuring sewer flows and water quality. This technology has the potential to provide water reclamation facilities with accurate and reliable data across all pipe flow conditions to inform decision-making on costly infrastructure and operations. In addition, by helping to reduce basement backups, this technology has broader social implications as low-income and minority communities are disproportionately affected by flood impacts. This I-Corps project is based on the development of a low-cost, video-based sensor to measure flows in sanitary sewer systems using machine learning and spectral analysis. Specifically, the proposed technology captures video of sewer flow and processes using a machine learning algorithm to estimate velocity and water level. In addition, it uses spectral analysis to determine the clarity and color of water that can be used to identify sources of pollutants within the sanitary sewer system. This technology is novel in that for the first-time a video-based sensor is applied to accurately measure flow and water quality in sanitary sewers. The proposed technology has the potential to advance water reclamation operations through less expensive, more reliable, and more accurate data on the magnitude and quality of water flow in sanitary sewers. 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.

NSF Program Director: Ruth Shuman