Abstract
The Horn of Africa region has frequently been affected by severe
droughts and food crises over the last several decades, and this will
increase under projected global-warming and socio-economic pathways.
Therefore, exploring novel methods of increasing early warning
capabilities is of vital importance to reducing food-insecurity risk. In
this study, we present the XGBoost machine-learning model to predict
food-security crises up to 12 months in advance. We used
>20 datasets and the FEWS IPC current-situation estimates
to train the machine-learning model. Food-security dynamics were
captured effectively by the model up to three months in advance (R2
> 0.6). Specifically, we predicted 20% of crisis onsets in
pastoral regions (n = 84) and 40% of crisis onsets in agro-pastoral
regions (n = 23) with a 3-month lead time. We also compared our 8-month
model predictions to the 8-month food-security outlooks produced by FEWS
NET. Over a relatively short test period (2020–2022), results suggest
the performance of our predictions is similar to FEWS NET for
agro-pastoral and pastoral regions. However, our model is clearly less
skilled in predicting food security for crop-farming regions than FEWS
NET. With the well-established FEWS NET outlooks as a basis, this study
highlights the potential for integrating machine-learning methods into
operational systems like FEWS NET.