Predicting Food Security with Machine Learning

Abstract

Hunger is on the rise throughout Africa, with famine threatening millions across several countries. Rapid and accurate identification of food insecurity crises can enable humanitarian responses to mitigate casualties from hunger and save lives. We develop a predictive model of food security based on readily available, spatially granular data on prices, geography, and demographics. Using machine learning techniques, we are able to improve the accuracy of predicting those villages that face a potential threat of hunger. As with any rare event, one challenge with predicting food insecurity is the low rate of severe food insecurity in the baseline data. We use several different approaches to address this imbalance to allow us to capture a higher fraction of these rare events. We apply our procedure to three sub-Saharan African