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

I-Corps项目的更广泛影响/商业潜力在于改善水回收作业，并对人类和环境健康产生相关的积极影响。美国的水回收设施每年向环境中排放数十亿加仑未经处理的废水，原因是大风暴导致其收集系统溢流。这些系统的故障还可能导致未经处理的水从地下室流入市民家中，这可能造成财产损失和严重疾病的风险。为了克服这些挑战，水回收设施依靠下水道流量数据来了解管网内发生的情况；然而，现有的传感器存在一些缺点，包括数据不准确、成本高、无法检测低流量。提出的技术旨在通过机器学习和基于视频的传感器来测量下水道流量和水质，从而克服这些挑战。该技术有可能为水回收设施提供准确可靠的数据，涵盖所有管道流动条件，为昂贵的基础设施和运营决策提供信息。此外，通过帮助减少地下室的备份，这项技术具有更广泛的社会意义，因为低收入和少数民族社区受到洪水影响的影响不成比例。I-Corps项目的基础是开发一种低成本、基于视频的传感器，利用机器学习和光谱分析来测量卫生下水道系统的流量。具体来说，该技术使用机器学习算法捕获下水道流量和过程的视频，以估计流速和水位。此外，它使用光谱分析来确定水的清晰度和颜色，可用于识别卫生下水道系统内污染物的来源。这项技术的新颖之处在于，它首次应用基于视频的传感器来精确测量卫生下水道的流量和水质。拟议的技术有可能通过更便宜、更可靠和更准确的卫生下水道水流大小和质量数据来推进水回收作业。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。

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.