Food insecurity in low-income countries is on the rise as climate variation and economic shocks, including the COVID-19 pandemic, take their toll. Accurately predicting when and where hunger crises occur is critical to effective humanitarian aid response. A new study from the University of Illinois explores how machine learning can help improve forecasting when used appropriately.
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Tech News 1: How machine learning can improve food insecurity predictions
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