Machine learning can guide food security efforts when primary data are not available - Nature Food

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Machine learning can guide food security efforts when primary data are not available - Nature Food
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Scientists propose machine learning approach to solve the world's food insecurity issue NatureFoodJnl

To conclude, this kind of analysis will allow WFP to identify the main factors behind a predicted change in insufficient food consumption or in crisis or above food-based coping. More specifically, WFP will be able to divide countries in various tiers of risks by combining levels of prevalence of insufficient food consumption and food-based coping with levels of deterioration over time.

Despite the encouraging results, several limitations apply. First of all, the proposed models are trained and validated combining together sub-national data from a number of different countries. Having such rich variety of data made it possible to build a global model that can be used to estimate the food security situation in any area in the world.

In regard to the secondary information feeding the proposed models, in this study, we resort to data on the three main drivers of food insecurity: economic shocks, extreme weather events and conflict. Undoubtedly, further information could be included to enrich the models, such as data on displacements, natural hazards, animal and crop diseases and epidemic outbreaks.

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