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

SBIR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。拟议的工作是建立一个金融体系，帮助城市和公用事业公司建设基础设施项目。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，利用环境、经济和社会数据为水质管理项目融资。拟议的项目为环境适应和水恢复项目提供分析和金融工具。它使用开源数据管理、传感器和接口，并在一个系统中使用最先进的人工智能数字孪生技术的数学模型。环境数据将使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。计量经济模型将不确定性分析与强化学习相结合，其中预测的准确性受到激励。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 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.