In March 1989, three separate seemingly unrelated events occurred resulting in a widespread electrical blackout for Northeastern Canada and the US. A  tripped circuit transformer blew up in the Hydro-Québec power grid left 6 million people without electricity. A week earlier, an unusually harsh snowstorm had strained the region; the day before, a solar flare and accompanying release of plasma and magnetic field sent a mountain of energy propelling toward Earth at a million miles an hour.

The complex interactions of these interconnected systems – environmental science, space weather and solar activity – pushed the electric power grid to a tipping point that could not be understood when only looking at these systems individually--not as a whole. within any single one of those systems.

The Predictive Risk Investigation System for Multilayer Dynamic Interconnection Analysis (PRISM) aims to harness data in order to identify risk factors across domains for catastrophic events such as the 1989 blackout – which impacted transportation, food, water, health and finance and racked up costs exceeding $2 billion. The National Science Foundation (NSF) funded the program and Ryan M. McGranaghan, Principal Data Scientist and Aerospace Engineering Scientist at ASTRA, LLC co-leads the project. ASTRA’s team of experts in fields of space weather and data science, combined with professionals in statistics, computer science, finance, energy, agriculture, ecology, hydrology, and climate, the PRISM effort will integrate data across different areas to improve risk prediction.

“We want to quantify the risks that the worst-case scenarios present, and ultimately use that understanding to improve the resilience of our human-natural systems,” said McGranaghan, who is a principal investigator on the two-year, $2.42 million grant, which emerged from the NSF’s Harnessing the Data Revolution Big Idea activity.

“Our approach is to identify systemic risks – those that tie together different domains and have the biggest spillover potential – and data-driven tools to better control them,”McGranaghan said. If systems had been in place to recognize the heightened risks caused by the snowstorm and the solar flare, the 1989 power outage may have been averted or at least minimized. Similarly, understanding the ways it affected systems such as health care and transportation could help policymakers plan a more effective response.

The transdisciplinary approach is essential, something McGranaghan likes to describe as antidisciplinary, meaning the space between fields, because today’s world is composed of highly interconnected and interdependent systems, and no single expert is equipped to identify the signs of risk or the full impact of catastrophes. The key is to use data science to help integrate information to find patterns. “Our technologically-dependent society operates in a new environment, one with increasing complexity and scale, and requires convergence between disciplines to understand when critical systems are stressed and strained and to prepare effective and timely responses,” said McGranaghan.

The researchers plan to assemble large datasets across sectors such as agriculture, climate and energy to create an interactive data library. Once they’ve developed this library, they’ll use cutting-edge data analysis to identify what they’ve called critical risk indicators – quantifiable information associated with risk exposure, particularly for potential catastrophes. They’ll also employ machine learning to look for anomalies in the data that might lead to new insights.

The researchers will then focus their efforts on identifying risk interconnections, and systemically important risk indicators across the different domains, in order to both predict potential hazards and to lessen the possible system-wide losses once they’ve occurred. Members of the team shared that they plan to examine known risk indicators and apply data science to identify new ones.

As part of the project, the researchers will work with stakeholders in the relevant fields, in hopes that policymakers would incorporate their findings. Their goal is to help create early warnings for catastrophes and improve preparedness for devastating events worldwide.