Forecasting Trends in Food Security: a Reservoir Computing Approach
- URL: http://arxiv.org/abs/2312.00626v2
- Date: Wed, 20 Dec 2023 15:05:07 GMT
- Title: Forecasting Trends in Food Security: a Reservoir Computing Approach
- Authors: Joschka Herteux, Christoph R\"ath, Amine Baha, Giulia Martini, Duccio
Piovani
- Abstract summary: We present a new 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 integrated global hunger monitoring system.
- Score: 0.8437187555622164
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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, livelihoods, and scarce financial resources. In
this work we present a new 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 integrated 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 across 90 countries (https://hungermap.wfp.org/). In this
study, we assessed the performance of various models including ARIMA, XGBoost,
LSTMs, CNNs, and Reservoir Computing (RC), by comparing their Root Mean Squared
Error (RMSE) metrics. This comprehensive analysis spanned classical
statistical, machine learning, and deep learning approaches. 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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