Paper/Poster/Presentation Title
Keywords
Nuisance flooding, Urban Flooding, Sea-level rise, Cyber-physical Systems, Machine Learning
Start Date
16-9-2020 4:40 PM
End Date
16-9-2020 5:00 PM
Abstract
Coastal communities are facing repetitive flooding caused by both tidal and rainfall-driven events. Climate change and sea level rise are resulting in more frequent and intense rainfall events, and sea-level rise is causing urban drainage infrastructure to be less effective during high tide periods when outfalls can be either partially or fully submerged. We are studying this problem and its impact on transportation and stormwater infrastructure systems within urban coastal communities. Our study region and partner in the research is the City of Norfolk, Virginia, USA. Norfolk is a historic town in coastal Virginia that is home to the largest Navy base in the world, the second busiest port on the United States East Coast, and is one of 100 Rockefeller Resilient Cities in the world. Our approach is to apply principles from the field of cyber-physical systems (CPS) to improve flood resiliency in coastal communities facing nuisance flooding. We are using real-time observational networks, crowdsourced data, physics-based and machine learning modeling approaches, model predictive control, and economic and social science methods to explore ways to better understand and mitigate the impacts of street-scale flooding. This presentation will provide a high-level overview of our research project including exploring (1) how real-time control of stormwater infrastructure systems can help to improve the resilience of these systems during nuisance flooding events by strategically holding back rainfall runoff and preventing tidally driven stormwater backups, (2) how both physics-based and machine-learning methods can be combined to real-time decision support, and (3) how reputation system approaches can be used to measure trust in crowdsourced rainfall datasets. These and related activities on the project are aimed at the common goal of leveraging real-time data from a variety of sources, innovative modelling techniques, and community-driven decision making to improve community resilience to nuisance flooding.
Advancing Flood Resilience in Urban Coastal Communities with Real-time Modeling, Monitoring, and Control
Coastal communities are facing repetitive flooding caused by both tidal and rainfall-driven events. Climate change and sea level rise are resulting in more frequent and intense rainfall events, and sea-level rise is causing urban drainage infrastructure to be less effective during high tide periods when outfalls can be either partially or fully submerged. We are studying this problem and its impact on transportation and stormwater infrastructure systems within urban coastal communities. Our study region and partner in the research is the City of Norfolk, Virginia, USA. Norfolk is a historic town in coastal Virginia that is home to the largest Navy base in the world, the second busiest port on the United States East Coast, and is one of 100 Rockefeller Resilient Cities in the world. Our approach is to apply principles from the field of cyber-physical systems (CPS) to improve flood resiliency in coastal communities facing nuisance flooding. We are using real-time observational networks, crowdsourced data, physics-based and machine learning modeling approaches, model predictive control, and economic and social science methods to explore ways to better understand and mitigate the impacts of street-scale flooding. This presentation will provide a high-level overview of our research project including exploring (1) how real-time control of stormwater infrastructure systems can help to improve the resilience of these systems during nuisance flooding events by strategically holding back rainfall runoff and preventing tidally driven stormwater backups, (2) how both physics-based and machine-learning methods can be combined to real-time decision support, and (3) how reputation system approaches can be used to measure trust in crowdsourced rainfall datasets. These and related activities on the project are aimed at the common goal of leveraging real-time data from a variety of sources, innovative modelling techniques, and community-driven decision making to improve community resilience to nuisance flooding.
Stream and Session
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