Governments and agencies are struggling to coordinate effective disaster relief programs. Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) can help. Here’s how.
The cost of natural disasters is at an all-time high
Natural disasters wreak havoc around the world every year. But it’s hard to appreciate the scale of this damage.
15 disaster events caused more than $22 billion in damages in the United States in 2017, according to the National Oceanic and Atmospheric Administration (NOAA). And that doesn’t include the Northern California Wildfires or the biggest hurricanes.
Hurricane Harvey’s price tag topped $125 billion, according to NOAA. Hurricanes Irma ($64-92 billion) and Maria ($40-80 billion) reduced much of Puerto Rico, the U.S. Virgin Islands, and the Caribbean to ruins.
And of course, these numbers only tell part of the story. The humanitarian impact is incalculable.
Relief agencies are struggling
In late September 2017, a week after Hurricane Maria made landfall in Puerto Rico, CNN reported that 10,000 containers containing food, water, medicine and other supplies were sitting on the tarmac at San Juan Port while officials struggled to coordinate distribution efforts.
This is not an isolated case. Governments around the world are struggling to coordinate effective, efficient programs. There’s lots of data out there, but making of use of it is easier said than done. Disaster data is often fragmented, incomplete, or difficult to access.
The scope is challenging, too. There’s just so much data out there.
Relief agencies and governments need to turn this information into useful insights. But while many techniques for processing crisis data already exist, this technology usually captures a limited amount or type of data, and only goes so far in coordinating disaster relief efforts.
The role of AI and machine learning in disaster relief
Recent advances in machine learning and artificial intelligence are allowing researchers, engineers, and scientists to access and analyze new and bigger data sources than ever before.
By combining AI and ML with data analytics technologies like natural language processing, experts are creating a new breed of AI. These systems enable their users to ask specific, targeted questions and receive useful answers drawn from messy, real-world datasets.
Governments and relief agencies are beginning to use these tools to coordinate better disaster relief programs. For example, large-scale behavior and movement data, run through predictive machine learning models, can help officials distribute supplies to where people are going, rather than where they were.
Predictive analytics programs like these are still in their early stages, but offer a promising new approach to disaster relief.
In short, the role of AI in disaster relief is to help governments and relief agencies parse through large volumes of complex, fragmented data to generate useful information that they can act on more quickly than before.
Using social data analytics and AI to respond faster to floods and other natural disasters
The researchers found that by combining data from disaster response organizations, the Global Flood Detection System (GFDS) satellite flood signal, and flood-related Twitter activity, disaster relief organizations can “gain a quicker understanding of the location, the timing, as well as the causes and impacts of floods.”
Of course, the sheer volume of Twitter data (350,000 tweets per minute) creates huge problems for anyone trying to make use of it. To solve this issue, some projects are turning to AI and machine learning.
AIDR (Artificial Intelligence for Digital Response) is an open source software platform built to “filter and classify social media messages related to emergencies, disasters, and humanitarian crises”. Sponsored by the United Nations Office for the Coordination of Humanitarian Affairs, AIDR uses supervised machine learning and artificial intelligence to tag thousands of social media messages per minute. This structured data is then ready for use in dashboards, maps, or other analytics programs.
Indeed, some of the most useful data generated during a crisis comes from social media users and on-the-ground aid workers. Images and comments from Twitter, Facebook, Instagram, and Youtube, for example, can help experts make initial damage assessments. This information can also help rescue workers find disaster victims more quickly, while identifying and mapping new disaster sites in need of aid.
Finally, combining data from satellite imagery, seismometers, with location-tagged social media comments can help relief organizations to provide early warnings and verify reports in real-time.
- Big Data for Climate Change and Disaster Resilience: Realizing the Benefits for Developing Countries – Data-Pop Alliance
- Why information and intelligence are vital in disaster relief – Rescue Global
- Artificial Intelligence for Disaster Response – AIDR, an open-source software project
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