Forecasting trends in food security with real time data

TL;DR

This paper introduces a data-driven methodology for forecasting food security levels at the sub-national level in four countries, comparing various models and highlighting Reservoir Computing as particularly effective.

Contribution

It presents a novel quantitative approach using real-time data and evaluates multiple models, identifying Reservoir Computing as especially suitable for food security forecasting.

Findings

Reservoir Computing outperforms other models in accuracy.

The methodology enables early detection of food insecurity.

Models show robustness with limited data samples.

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.