The frequency of recurrent nuisance flooding (RNF) events is increasing and accelerating along much of the US East and Gulf Coasts due to Sea Level Rise (SLR) and high tides. These recurrent floods overwhelm the stormwater drainage systems, cause road closures, pose a major threat to the built infrastructure, and disrupt communities. In this research, the near real-time processing of RNF data, longer term prediction of RNF at both street and community scale and effective sharing of this information will instill trust in local drivers and improve safety in the community. The main objective of this Smart & Connected Communities (SCC) project is to develop a Scalable Modeling and Adaptive Real-time Trust-based communication (SMARTc) system for roadway inundation detection and monitoring. The SMARTc system will be evaluated for a flood-prone region in the City of Norfolk, Virginia, using data from the City’s cameras, tide gauges, and existing and new overland water level sensors in the field. By having access to such information in near real-time, citizens will be able to avoid driving through flooded roads, emergency vehicles can be rerouted around inundated roads, and cities will have a better understanding of flooding patterns and the needs to invest in storm-water and coastal flood protection systems. The team will also engage with RISE – a non-profit organization in Norfolk focused on helping businesses develop new solutions for coastal communities to adapt to SLR and RNF. This collaboration is expected to expedite scaling up methods and technologies, and future transition to practice. In addition, various educational and outreach opportunities are planned to increase the project impact. These include a regional forum with participants representing a broad range of stakeholders, design projects for ongoing NSF REU programs, integration of research outcomes into undergraduate and graduate classes, hands on activities for visiting high school students, interdisciplinary capstone projects, and presentation of a prototype system to minority middle school students. The results of this research will be shared to the local community in Norfolk to increase awareness of RNF and technologies for increasing resilience to RNF. Once deployed in the field in Norfolk, the solutions could provide hundred-thousands of citizens, businesses, and emergency services in Hampton Roads with accurate roadway conditions under RNF. These solutions could then be adopted by other communities across the nation, potentially helping millions. The expected outcomes of this project are directly relevant to Harnessing the Data Revolution component of the NSF’s Ten Big Ideas.

This research will include the following tasks: (i) Novel machine learning algorithms for detecting floodwater extent and depth at street level in near real-time based on surveillance camera images collected under varying weather conditions; (ii) Hydrodynamic modeling integrating a coupled hydrologic-stormwater-coastal model to predict flood levels at street to community scales and real-time update of these predictions based on sensor and image data; (iii) Prediction of roadway capacities in real-time under partial inundations and correlation of floodwater depth and extent with driver behavior; and (iv) Effective communication of flood risk and road inundation to the public, leveraging granularity and uncertainty of flood information. The envisioned system will leverage sensor data and camera images for near real-time road inundation detection and will integrate the extracted dynamic information with hydrodynamic models for street to community-scale road inundation prediction. The outcome of the first task will yield near real-time learning model for street-scale RNF extent and depth recognition. The second task will yield an improved community-scale road network flood prediction model for RNF extent, depth, and flood duration using City camera and other sensor data. The third task will yield improved microscopic car-following models for partially flooded roadway segments so that the capacities and bottlenecks may be estimated and characterized accurately in near real-time. Finally, the fourth task is expected to offer effective ‘risk’ communication strategies for drivers using the RNF extent, depth. By having access to such information in near real-time, which is currently not available, citizens are expected to avoid driving through flooded roads, emergency vehicles can reroute around inundated roads, and cities will have a better understanding of flooding patterns and the needs to invest in storm-water and coastal flood protection systems.