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

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.