Forecasting trends in food security with real time data
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ID: 282978
2023
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Abstract
Early warning systems are an essential tool for effective humanitarian
action. Advance warnings on impending disasters facilitate timely and targeted
response which help save lives and livelihoods. In this work we present a
quantitative methodology to forecast levels of food consumption for 60
consecutive days, at the sub-national level, in four countries: Mali, Nigeria,
Syria, and Yemen. The methodology is built on publicly available data from the
World Food Programme's global hunger monitoring system which collects,
processes, and displays daily updates on key food security metrics, conflict,
weather events, and other drivers of food insecurity. In this study we assessed
the performance of various models including Autoregressive Integrated Moving
Average (ARIMA), Extreme Gradient Boosting (XGBoost), Long Short Term Memory
(LSTM) Network, Convolutional Neural Network (CNN), and Reservoir Computing
(RC), by comparing their Root Mean Squared Error (RMSE) metrics. Our findings
highlight Reservoir Computing as a particularly well-suited model in the field
of food security given both its notable resistance to over-fitting on limited
data samples and its efficient training capabilities. The methodology we
introduce establishes the groundwork for a global, data-driven early warning
system designed to anticipate and detect food insecurity.
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| Authors | Joschka Herteux; Christoph Räth; Giulia Martini; Amine Baha; Kyriacos Koupparis; Ilaria Lauzana; Duccio Piovani |
| Journal | arXiv |
| Year | 2023 |
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