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Predicting Food-Security Crises in the Horn of Africa Using Machine Learning
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  • Tim Sebastiaan Busker,
  • Bart van den Hurk,
  • Hans de Moel,
  • Marc van den Homberg,
  • Chiem van Straaten,
  • Rhoda A. Odongo,
  • Jeroen C.J.H. Aerts
Tim Sebastiaan Busker
Vrije Universiteit Amsterdam

Corresponding Author:[email protected]

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Bart van den Hurk
Deltares
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Hans de Moel
Vrije Universiteit Amsterdam
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Marc van den Homberg
510 an initiative of the Netherlands Red Cross
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Chiem van Straaten
Vrije Universiteit Amsterdam
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Rhoda A. Odongo
Vrije Universiteit Amsterdam
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Jeroen C.J.H. Aerts
Vrije Universiteit
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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.
03 Nov 2023Submitted to ESS Open Archive
08 Nov 2023Published in ESS Open Archive