英文标题：
《Impact of weather factors on migration intention using machine learning
algorithms》
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作者：
John Aoga, Juhee Bae, Stefanija Veljanoska, Siegfried Nijssen, Pierre
Schaus
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最新提交年份：
2020
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英文摘要：
A growing attention in the empirical literature has been paid to the incidence of climate shocks and change in migration decisions. Previous literature leads to different results and uses a multitude of traditional empirical approaches. This paper proposes a tree-based Machine Learning (ML) approach to analyze the role of the weather shocks towards an individual\'s intention to migrate in the six agriculture-dependent-economy countries such as Burkina Faso, Ivory Coast, Mali, Mauritania, Niger, and Senegal. We perform several tree-based algorithms (e.g., XGB, Random Forest) using the train-validation-test workflow to build robust and noise-resistant approaches. Then we determine the important features showing in which direction they are influencing the migration intention. This ML-based estimation accounts for features such as weather shocks captured by the Standardized Precipitation-Evapotranspiration Index (SPEI) for different timescales and various socioeconomic features/covariates. We find that (i) weather features improve the prediction performance although socioeconomic characteristics have more influence on migration intentions, (ii) country-specific model is necessary, and (iii) international move is influenced more by the longer timescales of SPEIs while general move (which includes internal move) by that of shorter timescales.
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中文摘要：
在实证文献中，气候冲击和移民决策变化的发生率越来越受到关注。以前的文献得出了不同的结果，并使用了大量传统的实证方法。本文提出了一种基于树的机器学习（ML）方法，以分析在布基纳法索、科特迪瓦、马里、毛里塔尼亚、尼日尔和塞内加尔等六个依赖农业的经济国家，天气冲击对个人移民意愿的影响。我们使用列车验证测试工作流程执行了几种基于树的算法（例如XGB、随机森林），以构建健壮且抗噪声的方法。然后，我们确定重要的特征，这些特征显示了它们影响迁移意图的方向。这种基于ML的估算考虑了不同时间尺度和各种社会经济特征/协变量的标准化降水蒸散指数（SPEI）捕捉到的天气冲击等特征。我们发现（i）天气特征提高了预测性能，尽管社会经济特征对移民意向有更大的影响，（ii）国家特定模型是必要的，（iii）国际迁移受SPEI较长时间尺度的影响更大，而一般迁移（包括内部迁移）受较短时间尺度的影响更大。
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分类信息：

一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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