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Forecasting trends in food security with real time data

Food Science and Technology

Forecasting trends in food security with real time data

J. Herteux, C. Raeth, et al.

This research, conducted by Joschka Herteux, Christoph Raeth, Giulia Martini, Amine Baha, Kyriacos Koupparis, Ilaria Lauzana, and Duccio Piovani, unveils a groundbreaking quantitative methodology for forecasting food consumption levels in Mali, Nigeria, Syria, and Yemen. Leveraging the WFP's real-time monitoring system, this study highlights the superior performance of Reservoir Computing in creating a robust early warning system for food insecurity.

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Playback language: English
Abstract
This research presents a quantitative methodology for forecasting sub-national food consumption levels (60 days) in Mali, Nigeria, Syria, and Yemen using the World Food Programme's (WFP) real-time monitoring system. Various models (ARIMA, XGBoost, LSTM, CNN, Reservoir Computing (RC)) were assessed; RC proved superior due to its resistance to data variations and efficient training. This methodology lays groundwork for a global, data-driven early warning system for food insecurity.
Publisher
Communications Earth & Environment
Published On
Oct 12, 2024
Authors
Joschka Herteux, Christoph Raeth, Giulia Martini, Amine Baha, Kyriacos Koupparis, Ilaria Lauzana, Duccio Piovani
Tags
food consumption
forecasting
Reservoir Computing
food insecurity
real-time monitoring

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