A new machine-learning algorithm can predict looming food insecurity more accurately than experts or traditional models alone.
The algorithm combs news stories for keywords that are linked to the causes of food shortages, including pests, conflict, and economic shocks. By combining its findings with traditional warning signs of a crisis, such as a lack of greenery captured by Earth-orbiting satellites, researchers can predict where aid will be needed as much as a year in advance.
“We can predict better and faster,” says Samuel Paul Fraiberger, a data scientist at the World Bank’s Development Data Group and the senior author of a new study describing the algorithm published in the journal Science Advances...
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