Many cities across the United States maintain large networks of stormwater basins, canals, and underground pipes. When it rains, this infrastructure moves water and pollutants away from buildings to nearby rivers and streams. In many cities, these stormwater systems are too small or too old, resulting in dangerous flooding and pollution of downstream rivers and lakes. Upgrading infrastructure is a costly alternative. Instead of relying on new construction, the PI will demonstrate how existing stormwater systems can be used more efficiently and made "smarter" through the use of wireless sensors, valves, and pumps to address this national issue. This approach will enable automatic control of stormwater systems to reduce flooding and improve water quality. To this end, the PI will investigate computational methods to embed stormwater systems with "intelligence," helping to reduce the cost of improving US infrastructure while protecting public health and the environment. The PI will also develop training programs for a new generation of technicians, who will deploy and maintain smart stormwater systems.
The PI will improve the fundamental understanding of the water flow and water quality benefits that can be achieved through the real-time control of urban watersheds. Results of this research will lead to a theoretical and conceptual framework to begin studying the impacts of autonomous technologies on the scale of entire watersheds. The research hypothesis of this proposal is that "smart" stormwater systems will vastly shrink the size of infrastructure required to manage runoff pollution. The hypothesis will be tested across three objectives: (1) investigate new algorithms, based on dynamical systems theory and reinforcement learning to control flows across city-scale stormwater systems, (2) study the distributed real-time control of water quality across the scale of entire watersheds with a focus on removing of sediment-bound and dissolved pollutants, and (3) explore how competing water flow and water quality goals can be unified to resiliently manage urban watersheds in real-time. Results of this research will allow entire watersheds to be controlled, akin to large distributed treatment plants, while vastly improving the fundamental understanding of the benefits, tradeoffs, and performance limits of "smart" water systems. All findings, data and algorithms will be shared freely with other researchers and practitioners through an open source web portal.
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.