Predicting Food Insecurity in Africa From Modis Imagery, Demographics, Economic Factors, Climate, and Supply Chain Information

Food insecurity in Africa is one of the major global-scale humanitarian disasters. The UN World Health Organization estimates that 37 million people in the horn of Africa are in acute hunger and describe the current situation as one of the worst hunger crises in the last 70 years. In this study we present a machine learning prediction using Gaussian processes and Bayesian model averaging to predict the probability and quantity of food insecurity for each country in Africa along with proposed locations for new transshipment nodes based on the current food supply chain. Additionally, we provide a taxonomy of the various types of predictor variables to infer importance of certain factors on food security. Our predictions are based on demographic, climate, environmental, financial, supply chain disruption, and remote sensing crop yield information, trained on historical food insecurity data. Proposed locations for additional transshipment locations is designed to optimize efficiency of the locations within the current supply chain. Results are shared publicly with a HTML interactive GIS.