融合冲突数据可提升非洲粮食安全预测准确率1.5%。
Improving the accuracy of food security predictions by integrating conflict data
- 用冲突数据训练机器学习模型，提升预测精度。
- 对比显示加入冲突信息后准确率提高1.5%。
- 适合关注地缘风险与粮食安全交叉研究者。
暴力和武装冲突已成为引发粮食危机的重要因素，但其影响程度仍缺乏深入探讨。本文针对非洲地区，基于饥荒早期预警系统网络（FEWSNET）与武装冲突地点事件数据（ACLED）进行综合相关性分析。结果表明，将冲突数据纳入机器学习模型训练，相比未引入冲突信息的模型，准确率提升1.5%。本研究的核心贡献在于对冲突对粮食安全预测影响的量化分析。
原文摘要 · Abstract (English)
Violence and armed conflicts have emerged as prominent factors driving food crises. However, the extent of their impact remains largely unexplored. This paper provides an in-depth analysis of the impact of violent conflicts on food security in Africa. We performed a comprehensive correlation analysis using data from the Famine Early Warning Systems Network (FEWSNET) and the Armed Conflict Location Event Data (ACLED). Our results show that using conflict data to train machine learning models leads to a 1.5% increase in accuracy compared to models that do not incorporate conflict-related information. The key contribution of this study is the quantitative analysis of the impact of conflicts on food security predictions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。