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Disaster Relief Is Dangerously Broken. Can AI Fix It?

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water flow model

Satellite imagery and other data approximate the depth, direction, and speed of flowing water to determine which areas are most at risk.

Credit: One Concern

The use of artificial intelligence in disaster relief is gaining favor as weather-related catastrophes grow in frequency and severity. For example, a startup founded by Stanford University's Ahmad Wani has launched a machine learning platform to help cities respond to floods with specialized maps that update in real time so emergency crews can determine where aid is most needed. Wani says a key challenge is enabling rapid, city-wide analysis of structural engineering to better predict damage.

The Flood Concern risk map was based on an earlier algorithm that digests building construction and retrofitting data, integrated with information on building materials and surrounding soil properties, to predict earthquake damage. Flood Concern crunches vast data volumes based on water-flow physics, previous flood data, and satellite imagery to approximate water depth, direction, and speed and identify areas at most risk; demographic data is layered atop the prediction so emergency planners can anticipate areas where residents are most likely at-risk.

From Fast Company
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