DisastersUnderstanding the Hidden Impact of Disasters
The September 2017 Hurricane Maria killed people, demolished homes, and destroyed infrastructure. But Maria also damaged the manufacturing plants of a major IV bag maker, plunging hospitals into supply shortage that didn’t ripple across the mainland United States until six months after the hurricane made landfall. Given the highly integrated nature of supply chains in the U.S., natural and man-made disasters can have unanticipated consequences that are every bit as serious as the immediate damage of the event itself.
In the weeks after Hurricane Maria hit Puerto Rico in September 2017, the world heard volumes about the storm’s impact in terms of lives lost, homes demolished, and infrastructure destroyed.
But Maria had one lesser-known consequence that proved just as critical.
Maria damaged the manufacturing plants of a major IV bag maker, plunging hospitals into supply shortage that didn’t ripple across the mainland United States until six months after the hurricane made landfall.
Given the highly integrated nature of supply chains in the U.S., natural and man-made disasters can have unanticipated consequences that are every bit as serious as the immediate damage of the event itself.
Critical infrastructure is everywhere. INL notes that as communities grow and evolve into complex networks of facilities, services and personnel, it’s increasingly challenging for emergency managers to know how a disruption to infrastructure or supply chains might affect the essential downstream services.
Now, INL researchers have developed a tool that will help emergency response managers anticipate the effects of these critical infrastructure dependencies and respond quickly after a disaster.
The All Hazards Analysis (AHA) software provides a framework and a methodology for mapping the relationships among vital and vulnerable assets.
Out of Sight, Out of Mind
“In many cases, critical infrastructure is all around us, but it is out of sight, out of mind,” said Ryan Hruska, a Critical Infrastructure Research senior scientist at INL. “There are interesting feedback loops and issues that can be created, and we don’t understand the consequences unless we poke around a little bit.”
To develop a model for a town, county or region, emergency managers direct AHA to gather the information it needs about the essential infrastructure in the area of interest. Then, AHA uses machine-learning-enabled processes to integrate facility and systems information from various sources into the framework.
These sources can be structured, such as a database detailing all the substations operated by a given power plant, or unstructured, such as newspaper articles, technical references, design standards or incident reports from a website. For unstructured sources, AHA uses natural language processing, where algorithms are taught to recognize patterns in and extract information from human language texts.
Because AHA can collect data autonomously, it frees infrastructure emergency managers from the onerous task of providing and entering data themselves.
Managers can then simulate the loss of an infrastructure component within the framework to understand cascading effects that impact downstream services.
As more information is added, AHA’s models can evolve, becoming more accurate.
