SBIR Phase I: A Digital Platform to Assess Water Quality at Urban-Watershed Interfaces

SBIR 第一阶段：评估城市流域界面水质的数字平台

• 批准号: 2152000
• 负责人: Sudhir Murthy
• 金额: $ 25.5万
• 依托单位: RESBONDS INTERNATIONAL CORPORATION
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2023-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152000&HistoricalAwards=false
• 关键词: SBIR; Phase; Digital; Platform; Assess

The broader impact of this SBIR Phase I project is to help manage water quality at the boundaries of cities and watersheds. The proposed work develops a financial system to help cities and public utilities build infrastructure projects. This system integrates physical data with artificial intelligence and advanced monitoring systems. It will serve as a scalable analytics platform using environmental, economic, and social data for financing projects in water quality management.The proposed project develops analytics and a financial instrument for environmental adaptation and water restoration projects. It uses open-source data management, sensors and interfaces, and mathematical models in a system with state-of-the-art artificial intelligence-enabled digital twin technology. The environmental data will be used in an integrated watershed model using principles of uncertainty analysis and neural network-based learning. The econometric model combines uncertainty analysis with reinforcement learning where accuracy in prognostics is incentivized.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.

SBIR第一期项目的更广泛影响是帮助管理城市和流域边界的水质。这项拟议的工作建立了一个金融体系，帮助城市和公用事业建设基础设施项目。该系统将物理数据与人工智能和先进的监控系统相结合。它将作为一个可扩展的分析平台，使用环境、经济和社会数据为水质管理项目提供资金。拟议的项目为环境适应和水恢复项目开发分析和金融工具。它在一个拥有最先进的人工智能数字孪生技术的系统中使用开源数据管理、传感器和接口以及数学模型。环境数据将利用不确定性分析和基于神经网络的学习原理在综合流域模型中使用。计量经济学模型结合了不确定性分析和强化学习，其中预测的准确性得到了激励。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。