According to the National Centers for Environmental Information, as of July 2022, nine climate disaster events exceeded $1 billion in losses. Hurricane Ian, which has a reported death of more than 100 people and caused as much as $47 billion in insured losses, could make it the most expensive storm in Florida’s history.
Since June 2022, floods in Pakistan have killed 1678 people and washed away villages and infrastructure leaving behind 3.4 million children at increased risk of waterborne diseases, drowning and malnutrition. Hurricane Fiona left 900,00 people without power in Puerto Rico.
With natural disasters globally becoming more and more prevalent and dangerous, the application of technology like artificial intelligence (AI) has the potential to prevent or mitigate the destruction.
According to Neil Sahota, IBM Master Inventor, lead artificial intelligence advisor at the United Nations, and co-founder of the AI for Good Global Summit, people see natural disasters as extreme, sudden events. But in reality, Sahota says that thousands of subtle, slow-moving clues indicate the likelihood and severity of a natural disaster.
“As humans, we’re wired for those fast-moving, immediate threats [..]; unfortunately, we’re not good with the slow-moving, long-term threats,” said Sahota. “Thankfully, AI is, and that’s why it has become a powerful tool in predicting natural disasters and enabling us to take steps to mitigate and even prevent it.”
Sahota uses wildfires as an example of how AI is good at processing vast amounts of data in real time and finding those subtle connections among the variables. “We tend to look at climate conditions, amount of brush and other potential fuels, and the area’s topography to assess the fire risk,” said Sahota. “But as more AI wildfires tools have been developed, we have learned about many other factors involved, including ignition.”
Sahota says data from research with mining companies shows that lightning strikes can be a major source of wildfires, but how can we assess something so random?
“People may struggle with this, but AI has had a much easier time predicting where lightning storms may occur, the likelihood of where it will hit the ground, and what are the “hotspots” that would catch on fire,” said Sahota. “As a result, we now examine far more ignition sources like static electricity, hot surfaces and even friction to assess the threat of a wildfire.”
Sahota maintains that AI can prevent the next natural disaster by determining the ripple effect or the indirect impacts of a potential natural disaster.
“Let’s look at coastal flooding,” said Sahota. “We often use sea levels and the key metric, but recently saw an outbreak of Lyme disease in the southeastern of the US in places where Lyme disease was virtually nonexistent.”
Sahota said that as they studied the issue, they discovered that the ticks that typically have the illness migrated from the coasts to further inland. “A subtle change in the coastal environment occurred, but it was not so subtle to the ticks,” added Sahota.
“Tapping into this and leveraging AI, scientists are building a better picture of how one event can ripple through the whole ecosystem,” said Sahota. “Now, we have AI studying marine life, currents, and even ocean temperatures to find those subtle clues about a potential flood.”
From anticipation to optimizing relief resources to understanding hazards, AI can help detect and prepare for extreme weather and other hazards. A team at Lancaster University created a disaster mapping and damage detection system that allows rescue teams to prioritize designated areas in their relief efforts. The platform is powered by crowdsourced labeled data – road blockage, flooded areas, damaged buildings – marked by volunteers on the ground.
“Hurricane Ian [..] is an example of where hybrid intelligence, the combination of human and AI strengths, may have helped mitigate some of the impacts,” said Sahota.
“AI’s real-time capability to factor in thousands of climate data would’ve helped enable us to better predict the shift from a tropical storm to a hurricane to tropical storm back to a hurricane,” said Sahota. “This would have given us a better chance to prepare in the Carolinas and perhaps even for Florida.”
Sahota says that AI also would have helped on several fronts. “Using AI, we could hone the trajectory of the hurricane earlier [..] which would have helped quickly deploy medical resources and food and water before the hurricane hit,” said Sahota. “And then also leverage AI’s ability as a communication coach to engage [..] people with the right [..] motivations to evacuate in time, which hopefully would’ve reduced the number of deaths.”
“With a strong level of confidence, knowing the high-risk areas of devastation, we could better prepare on where to deploy resources for a faster recovery time on basic services like water, electrical power, and food supplies,” said Sahota.
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