As natural disasters grow more severe across the country, local governments are increasingly using predictive analytics to understand where and when an emergency will impact their communities.


Copyright: – “Can Artificial Intelligence Outsmart Natural Disasters?”


When fires start in Gilpin County, Colo., they burn hot and grow fast. Floods in Texas’ San Antonio River basin spill across highways, blocking emergency responders, and Norfolk, Va., sees homes inundated by coastal storms. Earthquakes shaking the Pacific Northwest risk derailing trains, injuring residents and causing power outages at hospitals.

Prediction and early detection tools — as well as automated responses — aim to help local governments reduce the damages of these kinds of natural disasters. Today’s tools are warning residents, triggering mitigation and helping first responders react more effectively. As artificial intelligence (AI) advances, sensors proliferate and data collections grow, prediction and detection technologies are likely to become more precise and effective.

Tarek Ghani, assistant professor of strategy at Washington University’s Olin Business School, and Grant Gordon, former senior director of innovation strategy at the International Rescue Committee, envision using AI to predict disasters in advance, thus enabling responders to take swifter actions to prevent or mitigate them.

Such tools could also anticipate how the crises would develop, guiding responders to be more effective in their interventions, they write in their 2021 article Predictable Disasters: AI and the Future of Crisis Response.

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Not all disasters are equally accessible to AI, however, and the technology is most reliable at analyzing events for which the root causes are well understood, plenty of data is available to train the algorithms and instances are recurrent enough that the models’ predictions can be compared against reality and fine-tuned, Ghani and Gordon write.

Floods are a strong example.

The San Antonio River Authority uses a forecasting model with data from the National Weather Service to predict floods in near real-time.[…]


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