A machine learning approach to scenario analysis and forecasting of mixed migration

IF 1.3 4区 计算机科学 Q1 Computer Science
R. Nair;B. S. Madsen;H. Lassen;S. Baduk;S. Nagarajan;L. H. Mogensen;R. Novack;R. Curzon;J. Paraszczak;S. Urbak
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引用次数: 5

Abstract

The development of MM4SIGHT, a machine learning system that enables annual forecasts of mixed-migration flows, is presented. Mixed migration refers to cross-border movements of people that are motivated by a multiplicity of factors to move including refugees fleeing persecution and conflict, victims of trafficking, and people seeking better lives and opportunity. Such populations have a range of legal status, some of which are not reflected in official government statistics. The system combines institutional estimates of migration along with in-person monitoring surveys to establish a migration volume baseline. The surveys reveal clusters of migratory drivers of populations on the move. Given macrolevel indicators that reflect migratory drivers found in the surveys, we develop an ensemble model to determine the volume of migration between source and host country along with uncertainty bounds. Using more than 80 macroindicators, we present results from a case study of migratory flows from Ethiopia to six countries. Our evaluations show error rates for annual forecasts to be within a few thousand persons per year for most destinations.
混合迁移情景分析与预测的机器学习方法
介绍了MM4SIGHT的开发情况,这是一个机器学习系统,可以对混合移民流进行年度预测。混合移民是指受多种因素驱使的人员跨境流动,包括逃离迫害和冲突的难民、贩运人口的受害者以及寻求更好生活和机会的人。这些人口有一系列的法律地位,其中一些没有反映在政府的官方统计数据中。该系统将机构对移民的估计与现场监测调查相结合,以建立移民量基线。调查显示,流动人口中有成群的移民驱动因素。考虑到反映调查中发现的移民驱动因素的宏观指标,我们开发了一个综合模型来确定来源国和东道国之间的移民量以及不确定性边界。利用80多项宏观指标,我们介绍了从埃塞俄比亚到六个国家的移民流动案例研究的结果。我们的评估显示,大多数目的地的年度预测误差率在每年几千人以内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IBM Journal of Research and Development
IBM Journal of Research and Development 工程技术-计算机:硬件
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: The IBM Journal of Research and Development is a peer-reviewed technical journal, published bimonthly, which features the work of authors in the science, technology and engineering of information systems. Papers are written for the worldwide scientific research and development community and knowledgeable professionals. Submitted papers are welcome from the IBM technical community and from non-IBM authors on topics relevant to the scientific and technical content of the Journal.
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