TNO Defense, Security and Safety, the Netherlands; Department of Experimental Psychology, Utrecht University, the Netherlands; 510, an initiative of the Netherlands Red Cross, the Netherlands.
510, an initiative of the Netherlands Red Cross, the Netherlands.
Sci Total Environ. 2021 Sep 10;786:147366. doi: 10.1016/j.scitotenv.2021.147366. Epub 2021 Apr 27.
Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.
由于人为冲突、气候变化和经济衰退，粮食不安全问题日益严重。预测粮食不安全状况对于能够提前采取行动至关重要，例如，由人道主义行为者采取行动。为了衡量粮食不安全的实际状况，目前使用专家和共识为基础的方法和调查。这两种方法都需要大量的人力、时间和预算。本文介绍了一种极端梯度增强机器学习模型，用于预测埃塞俄比亚粮食安全状况在一个月内的变化，空间粒度为生计区，提前期为 1 至 12 个月，使用开源数据。粮食安全状况的变化，以下简称预测变量，由综合粮食安全阶段分类数据表示。从 19 类数据集，得出 130 个变量作为粮食安全状况变化的预测变量。预测变量代表气候和土地、市场、冲突、基础设施、人口统计和生计区特征的变化。发现最相关的预测变量是粮食安全历史和地表土壤湿度。总体而言，与基线相比，该模型在预测粮食安全状况恶化和改善方面表现最佳。与最佳基线（哑分类器）相比，该方法在预测恶化方面的性能至少要好两倍（F1 宏评分）。与短期（3 个月；F1 宏平均 = 0.51）相比，该模型在预测长期（7 个月；F1 宏平均 = 0.61）时表现更好。将机器学习、监测系统的综合阶段分类 (IPC) 评级和开放数据相结合，可以为现有的基于共识的预测方法增加价值，因为这种组合提供了更长的提前期和更定期的更新。我们的方法也可以转移到其他国家，因为大多数预测变量的数据都可以从全球数据存储库中公开获得。
