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：早期预警系统是开展有效人道主义行动的重要工具。针对即将发生的灾难提供提前预警，有助于及时且有针对性地采取应对措施，从而挽救生命、保障生计并节约稀缺的财政资源。本研究提出了一种新的定量方法，用于在马里、尼日利亚、叙利亚和也门这四个国家的次国家级层面上，对未来连续60天的粮食消费水平进行预测。该方法基于世界粮食计划署全球饥饿综合监测系统（网址：https://hungermap.wfp.org/）公开收集、处理并展示的每日更新的关键粮食安全指标、冲突、天气事件及其他90个国家粮食不安全驱动因素的数据。在本研究中，我们通过比较包括ARIMA、XGBoost、LSTM、CNN及储备池计算（Reservoir Computing, RC）等多种模型的均方根误差（RMSE）指标，对其性能进行了评估。这项综合分析涵盖了经典的统计学方法、机器学习和深度学习方法。我们的研究结果表明，储备池计算因其在有限样本数据上显著的抗过拟合能力以及高效的训练性能，被证明是特别适用于粮食安全领域的模型。我们所提出的方法为建立一个旨在预测和检测粮食不安全状况的全球性、数据驱动的早期预警系统奠定了基础。