For floods, many emergency planners rely on static risk maps, which model the flow of water by assuming that rain is falling constantly everywhere. The sad state of emergency responseĮmergency planners have little technology to work with when it comes to understanding what is happening during a disaster. With that kind of information, planners can figure out which areas should be evacuated, where to put shelters, and what critical infrastructure–like schools or hospitals–needs the most help when flooding begins. Layered on top of the damage prediction is demographic data, so that emergency planners can see what areas of a city might have particularly vulnerable populations, like a significant percentage of seniors or disabled citizens. Now, the company is launching Flood Concern, a constantly evolving risk map that crunches huge amounts of data based on the physics of how water flows, information about previous floods, and even satellite imagery to approximate the depth, direction, and speed of the water–and determine which areas of a city are most at risk. Seismic Concern predicts the damage caused by earthquakes on a block-by-block level and is now used by eight different municipalities, including the cities of San Francisco, Los Angeles, and Cupertino. Wani, Hu, and Frank started One Concern in 2015 and then released its earthquake platform, called Seismic Concern, in 2016. (Of course, they won’t know for sure until a major earthquake hits.) With more data over time, that number will improve, but the team believes that it’s good enough to paint a broad picture of damage immediately after a quake. The trade-off is accuracy: Hu estimates that the algorithm is only about 85% accurate. Then, when a quake hits, the model absorbs new information coming from on-the-ground emergency responders, 911 calls, or even Twitter to make its predictions of the damage more accurate.īecause the model identifies patterns by looking through large amounts of data, it needs less computing power than the previous method of asking a computer to perform complex physics equations to understand how shaking will impact a structure. This data is combined with information on the building’s materials and surrounding soil properties to extrapolate what happens to this system when shaking occurs. Wani teamed up with fellow Stanford students Nicole Hu, a computer scientist who focuses on machine learning, and Tim Frank, an earthquake engineer, to build an algorithm that can digest data about how a building was built and how it’s been retrofitted over time. He decided to focus first on earthquakes, which are more of a threat than floods in California. We wanted to do it in three to five minutes.” “We didn’t have seven days or seven years. “We had to recreate that for the entire city” for the idea to work, Wani says.
But he had a problem: analyzing a single building using traditional structural engineering software took seven days on Stanford’s supercomputer. The idea was that if city officials could anticipate which areas would be most harmed, they would be able to deploy resources faster and more efficiently throughout the disaster zone.
He began contemplating how to predict a disaster’s damage. From earthquakes to floodsĪfter surviving the devastating flood in Kashmir, Wani returned to Stanford, where he was studying structural engineering. Artificial intelligence, such as the platform One Concern has developed, offers a tantalizing solution. As climate change heralds more devastating natural disasters, cities will need to rethink how they plan for and respond to disasters. Since 2000, more than 1 million people have perished from these extreme weather events.
In 2017 alone, these disasters cost the country $306 billion. has suffered from 219 climate disasters that cost over $1 billion, with the total cost exceeding $1.5 trillion.
It’s the latest in a wave of AI-powered tools aimed at helping cities prepare for an era of severe, and increasingly frequent, disasters.
The maps update in real-time based on data about where water is flowing to estimate where people need help the most. Today, Wani’s startup One Concern is launching a machine learning platform that provides cities with specialized maps to help emergency crews decide where to focus their efforts in a flood. “There is no science behind how people should be rescued,” he says. After this horrifying experience, Wani was struck by just how disorganized the emergency response was.