Potential and limitations of machine learning modeling for forecasting Acute Food Insecurity
Acute Food Insecurity (AFI) remains a highly relevant and persistent challenge. Machine Learning (ML) presents promising solutions to improve predictions and early warning systems by integrating large and diverse datasets and considering multiple drivers of AFI. This review examines target variables and input features in existing ML modeling efforts, providing an assessment of current data availability, accessibility and fragmentation, and improving the understanding of possibilities and limitations of ML for end-users. For modelers, we recommend optimal input variables and outline the modeling workflow by comparing all approaches. We furthermore develop a quantitative comparison of the influence of drivers in studied models’ predictions. We advocate for an increased effort to investigate ML causality and improve usability of ML models.
MACHEFER Melissande;
THOMAS Anne-Claire;
MERONI Michele;
VEIGA LOPEZ-PENA José Manuel;
RONCO Michele;
CORBANE Christina;
REMBOLD Felix;
2025-09-26
ELSEVIER
JRC138360
2211-9124 (online),
https://www.sciencedirect.com/science/article/pii/S2211912425000343,
https://publications.jrc.ec.europa.eu/repository/handle/JRC138360,
10.1016/j.gfs.2025.100859 (online),
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