Computing Resilience in an Era of Uncertainty

In an era of frequent, powerful storms, fast-spreading wildfires and global pandemics, communities are discovering their vulnerabilities when they can least afford it.

“We need to rethink what it means to be resilient. I use the boxing analogy ‘roll with the punches’: the ability to absorb the shocks of extreme events and recover quickly,” says Michel Boufadel, the director of NJIT’s Center for Natural Resources. “But to do so, the whole system needs to work together. It doesn’t matter if the power stays on, but 90% of the roads are closed.”

With collaborators at Rutgers and Princeton, Boufadel has developed a Community Intrinsic Resilience Index (CIRI) that will help cities and regions reduce the potential impacts of natural disasters by making strategic investments in infrastructure. The researchers assess resilience in four key sectors — transportation, energy, health care and socio-economics — they deem important to remaining productive and quickly returning to normal.

The team evaluates disaster-relevant factors in each sector and assigns values to them. In transportation, the amount of roadway per square mile and population, and the availability of public transit are some of the elements; in health care, the number of hospital beds, doctors and nurses per patient population and the percentage of residents with health insurance; in energy, the amount of underground wires and the availability of backup power. By analyzing socio-economic demographics, they can project the number of jobs potentially affected.

“We set thresholds for each sector. Going under one could potentially shut down a community,” notes Firas Gerges, a former Ph.D. student of Boufadel’s who is now a postdoctoral researcher in computer science at Princeton. Hani Nassif, director of the Rutgers Infrastructure Monitoring and Evaluation Group, is another collaborator.

The group recently computed CIRI scores for counties in New Jersey, which ranged between 63% and 80%, based on preliminary data. In the study, a post-disaster CIRI calculation following projected major flooding revealed that the transportation and socio-economic attributes of two coastal counties would fall below specified thresholds due to projected road closures and harm to local economies.

Their goal, Boufadel says, is to help local leaders and other policy makers integrate resilience within the planning and design phases of disaster management. “There is not enough money to avoid harm entirely, and decision-makers need numbers in order to prioritize spending,” he notes. “Should they spend money protecting the golf course or the high school?”

The team is currently collecting more energy data from local, nonprofit and government stakeholders so they can better assess energy resilience throughout the state and identify communities in need of both improved capacity and rapidly deployable energy supplies. Suggested solutions include mobile energy storage and megawatt-scale batteries that could be charged off-peak and placed where needed, such as close to commuter lines during weekdays.

“This is a crucial step to tackle energy budget deficits and attain energy equity, particularly in underserved communities within New Jersey,” Boufadel says.

Because natural disasters transcend political boundaries, the group has developed measures to assess resilience on the census tract level. Gerges explains: “We can calculate CIRI census block by census block and compile that to see how a region of a state would perform.”

They are currently developing and validating a new model that combines social and engineering concepts to measure the resilience of areas and infrastructures under different degrees of stress and at different temporal scales.

“We want to be able to say what’s resilient for a 10-year storm versus a 50-year storm,” Gerges says. “Using machine learning, we can predict climate variables decades into the future in New Jersey, such as the amount of precipitation in the Hackensack-Passaic Watershed, wind speed, temperature and solar irradiance. The latter will allow us to locate the best spots for solar farms under different climate scenarios.”