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.

翻译：早期预警系统是有效人道主义行动的关键工具。对即将发生的灾害进行提前预警，有助于及时、有针对性地采取应对措施，从而挽救生命、生计和宝贵的财政资源。本研究提出了一种新的定量方法，用于预测马里、尼日利亚、叙利亚和也门四个国家次国家级区域连续60天的粮食消费水平。该方法基于世界粮食计划署综合全球饥饿监测系统（https://hungermap.wfp.org/）的公开数据，该系统收集、处理并展示90个国家的每日粮食安全关键指标、冲突、天气事件及其他粮食不安全驱动因素的更新数据。本研究通过比较ARIMA、XGBoost、LSTM、CNN和储层计算（RC）等模型的均方根误差（RMSE）指标，评估了各模型的性能。这一综合分析涵盖了经典统计学、机器学习和深度学习方法。研究结果表明，储层计算因其在有限数据样本上显著的抗过拟合能力和高效训练特性，成为粮食安全领域中尤为适用的模型。本研究提出的方法为构建全球性、数据驱动的早期预警系统奠定了基础，该系统旨在预测和检测粮食不安全状况。