How AI Is Being Used to Respond to Natural Disasters in Cities
The number of people living in urban areas has tripled in the last 50 years, meaning when a major natural disaster such as an earthquake strikes a city, more lives are in danger. Meanwhile, the strength and frequency of extreme weather events has increased—a trend set to continue as the climate warms. That is spurring efforts around the world to develop a new generation of earthquake monitoring and climate forecasting systems to make detecting and responding to disasters quicker, cheaper, and more accurate than ever.
[time-brightcove not-tgx=”true”]On Nov. 6, at the Barcelona Supercomputing Center in Spain, the Global Initiative on Resilience to Natural Hazards through AI Solutions will meet for the first time. The new United Nations initiative aims to guide governments, organizations, and communities in using AI for disaster management.
The initiative builds on nearly four years of groundwork laid by the International Telecommunications Union, the World Meteorological Organization (WMO) and the U.N. Environment Programme, which in early 2021 collectively convened a focus group to begin developing best practices for AI use in disaster management. These include enhancing data collection, improving forecasting, and streamlining communications.
“What I find exciting is, for one type of hazard, there are so many different ways that AI can be applied and this creates a lot of opportunities,” says Monique Kuglitsch, who chaired the focus group. Take hurricanes for example: In 2023, researchers showed AI could help policymakers identify the best places to put traffic sensors to detect road blockages after tropical storms in Tallahassee, Fla. And in October, meteorologists used AI weather forecasting models to accurately predict that Hurricane Milton would land near Siesta Key, Florida. AI is also being used to alert members of the public more efficiently. Last year, The National Weather Service announced a partnership with AI translation company Lilt to help deliver forecasts in Spanish and simplified Chinese, which it says can reduce the time to translate a hurricane warning from an hour to 10 minutes.
Besides helping communities prepare for disasters, AI is also being used to coordinate response efforts. Following both Hurricane Milton and Hurricane Ian, non-profit GiveDirectly used Google’s machine learning models to analyze pre- and post-satellite images to identify the worst affected areas, and prioritize cash grants accordingly. Last year AI analysis of aerial images was deployed in cities like Quelimane, Mozambique, after Cyclone Freddy and Adıyaman, Turkey, after a 7.8 magnitude earthquake, to aid response efforts.
Read more: How Meteorologists Are Using AI to Forecast Hurricane Milton and Other Storms
Operating early warning systems is primarily a governmental responsibility, but AI climate modeling—and, to a lesser extent, earthquake detection—has become a burgeoning private industry. Start-up SeismicAI says it’s working with the civil protection agencies in the Mexican states of Guerrero and Jalisco to deploy an AI-enhanced network of sensors, which would detect earthquakes in real-time. Tech giants Google, Nvidia, and Huawei are partnering with European forecasters and say their AI-driven models can generate accurate medium-term forecasts thousands of times more quickly than traditional models, while being less computationally intensive. And in September, IBM partnered with NASA to release a general-purpose open-source model that can be used for various climate-modeling cases, and which runs on a desktop.
AI advances
While machine learning techniques have been incorporated into weather forecasting models for many years, recent advances have allowed many new models to be built using AI from the ground-up, improving the accuracy and speed of forecasting. Traditional models, which rely on complex physics-based equations to simulate interactions between water and air in the atmosphere and require supercomputers to run, can take hours to generate a single forecast. In contrast, AI weather models learn to spot patterns by training on decades of climate data, most of which was collected via satellites and ground-based sensors and shared through intergovernmental collaboration.
Both AI and physics-based forecasts work by dividing the world into a three-dimensional grid of boxes and then determining variables like temperature and wind speed. But because AI models are more computationally efficient, they can create much finer-grained grids. For example, the the European Centre for Medium-Range Weather Forecasts’ highest resolution model breaks the world into 5.5 mile boxes, whereas forecasting startup Atmo offers models finer than one square mile. This bump in resolution can allow for more efficient allocation of resources during extreme weather events, which is particularly important for cities, says Johan Mathe, co-founder and CTO of the company, which earlier this year inked deals with the Philippines and the island nation of Tuvalu.
Limitations
AI-driven models are typically only as good as the data they are trained on, which can be a limiting factor in some places. “When you’re in a really high stakes situation, like a disaster, you need to be able to rely on the model output,” says Kuglitsch. Poorer regions—often on the frontlines of climate-related disasters—typically have fewer and worse-maintained weather sensors, for example, creating gaps in meteorological data. AI systems trained on this skewed data can be less accurate in the places most vulnerable to disasters. And unlike physics-based models, which follow set rules, as AI models become more complex, they increasingly operate as sophisticated ‘black boxes,’ where the path from input to output becomes less transparent. The U.N. initiative’s focus is on developing guidelines for using AI responsibly. Kuglitsch says standards could, for example, encourage developers to disclose a model’s limitations or ensure systems work across regional boundaries.
The initiative will test its recommendations in the field by collaborating with the Mediterranean and pan-European forecast and Early Warning System Against natural hazards (MedEWSa), a project that spun out of the focus group. “We’re going to be applying the best practices from the focus group and getting a feedback loop going, to figure out which of the best practices are easiest to follow,” Kuglitsch says. One MedEWSa pilot project will explore machine learning to predict the occurrence of wildfires an area around Athens, Greece. Another will use AI to improve flooding and landslide warnings in the area surrounding Tbilisi city, Georgia.
Read more: How the Cement Industry Is Creating Carbon-Negative Building Materials
Meanwhile, private companies like Tomorrow.io are seeking to plug these gaps by collecting their own data. The AI weather forecasting start-up has launched satellites with radar and other meteorological sensors to collect data from regions that lack ground-based sensors, which it combines with historical data to train its models. Tomorrow.io’s technology is being used by New England cities including Boston, to help city officials decide when to salt the roads ahead of snowfall. It’s also used by Uber and Delta Airlines.
Another U.N. initiative, the Systematic Observations Financing Facility (SOFF), also aims to close the weather data gap by providing financing and technical assistance in poorer countries. Johan Stander, director of services for the WMO, one of SOFF’s partners, says the WMO is working with private AI developers including Google and Microsoft, but stresses the importance of not handing off too much responsibility to AI systems.
“You can’t go to a machine and say, ‘OK, you were wrong. Answer me, what’s going on?’ You still need somebody to take that ownership,” he says. He sees private companies’ role as “supporting the national met services, instead of trying to take them over.”