标题： 预测叙利亚和也门境内流离失所人口的迁移模式 显示英文标题

标题： Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen

摘要： 武装冲突导致国内流离失所者（IDPs）的数量前所未有——这些是被迫离开家园但仍在其国家内部的人。 IDPs经常迫切需要庇护所、食物和医疗保健，但预测大量IDPs何时会进入一个地区仍然是援助组织面临的主要挑战。 准确预测IDP迁移将使人道主义援助团体在冲突期间更有效地分配资源。 我们表明，使用公开数据可以提前一个月准确预测叙利亚和也门省内每月的IDP流动。 我们使用粮食价格、燃料价格、工资、地理空间和新闻数据来建模每月的IDP流动。 我们发现机器学习方法比基线持续性模型更能准确预测迁移趋势。 因此，我们的研究结果可能有助于在预测到IDP到达之前主动分配援助。 显示英文摘要

摘要： Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challenge for aid delivery organizations. Accurate forecasting of IDP migration would empower humanitarian aid groups to more effectively allocate resources during conflicts. We show that monthly flow of IDPs from province to province in both Syria and Yemen can be accurately forecasted one month in advance, using publicly available data. We model monthly IDP flow using data on food price, fuel price, wage, geospatial, and news data. We find that machine learning approaches can more accurately forecast migration trends than baseline persistence models. Our findings thus potentially enable proactive aid allocation for IDPs in anticipation of forecasted arrivals.