PFI:BIC Self-Learning Algorithms for Advancement of Smart Stormwater Green Infrastructure Systems

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A pressing social and environmental issue on a national scale is the effect of stormwater on waterbody impairment, particularly in urban and suburban watersheds. For example, stormwater can diminish water quality by discharging pollutants like metals and nutrients as it runs over land and into the streams. Stormwater causes stream erosion, sedimentation, flooding and overflows in combined sewer systems. To combat these issues, municipalities must adopt innovative technologies, such as Green Infrastructure (GI) systems like bioretention, constructed wetlands, and vegetated roofs. Despite the advantages of GI systems, its adoption has been slow due to technological and human factors. These systems are not yet dynamic, cannot adapt to seasonal changes and are often able to accomplish only one performance goal resulting in high implementation and maintenance costs. Other potential factors include operation and maintenance issues, policy and financing issues, lack of buy-in from different stakeholders, and unclear return on investment. This research will develop "smart" (i.e., efficient, active and self-learning) stormwater service systems. "Smart" systems use sensor- and human-generated data to streamline GI maintenance programs to be less costly and more effective in performance, prediction and failure prevention. The broader impacts of the research activities are the improvement of stormwater management across treatment scales. The research activities will lead to more efficient and economical GI that demonstrates its need and benefit to society and all watershed stakeholders by making major progress towards flood mitigation and water quality improvement in impaired waterbodies. The underlying technologies allow for unique opportunities to directly connect infrastructure to stakeholders, system data is transmitted, stored, and processed in cloud-based data management systems and published as web services. This project enlists a research-based approach that integrates application with the socio-technical system. This outcome will be achieved by optimally using all physical processes in a GI system (i.e., detention, infiltration, evapotranspiration), which uses sensors and controls integrated with real-time weather and system conditions, forecast data, and social media. Villanova University GI systems (green roof, constructed stormwater wetland, bioretention rain garden) will be equipped with sensors (e.g., soil moisture, water level, temperature and dissolved oxygen), as well as automated control structures (e.g., valves or gates), providing dynamic control algorithms that optimally operate during and after rain events. This GI system will be dynamically linked to a platform technology with real time visualization, data accessibility, quality assurance and real time control through physical computing. The entire automated system will operate at three time scales: 1) hours during, 2) days after the rain event, and 3) seasonal scale. All control algorithms will be geared to maximize storage available for stormwater and water quality improvement. These goals will vary across season and climate zones. The primary partners include Department of Civil and Environmental Engineering, and Department of Computing Sciences from Villanova University (Villanova, PA); University of Pennsylvania School of Design (Philadelphia, PA); and Geosyntec Consultants, an industry partner (Boston, MA). The broader context partners include Veolia, Paris France; City of Austin, Texas; District Department of the Environment; City of Omaha, Nebraska; and Philadelphia Water Department.