论文标题
天气因素对使用机器学习算法迁移意图的影响
Impact of weather factors on migration intention using machine learning algorithms
论文作者
论文摘要
在经验文献中越来越关注的是气候冲击的发生和移民决策的变化。先前的文献导致不同的结果，并采用了多种传统的经验方法。 本文提出了一种基于树木的机器学习（ML）方法，以分析天气冲击对个人在六个农业依赖性经济国家中迁移的意图的作用，例如布基纳法索，象牙海岸，马里，毛里塔尼亚，尼日尔和塞内加尔。我们使用火车验证测试工作流程来构建多种基于树的算法（例如，XGB，随机森林）来构建强大的耐噪声方法。然后，我们确定重要的特征，显示它们在哪个方向影响迁移意图。这项基于ML的估计是针对不同时间尺度和各种社会经济特征/协变量的标准降水蒸发指数（SPEI）捕获的诸如天气冲击的特征。 我们发现（i）天气特征改善了预测性能，尽管社会经济特征对迁移意图有更大的影响，（ii）国家特定模型是必要的，（iii）国际举动受到SPEIS较长的时间表的影响更大，而一般移动（包括内部移动）则由较短的时间表的时间影响。
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.
