通过混合方法支持迁移政策：通过混合方法预测欧洲非法边境穿越
Supporting Migration Policies with Forecasts: Illegal Border Crossings in Europe through a Mixed Approach
AI总结：
本文提出一种结合机器学习与专家判断的混合方法，用于预测欧洲非法边境穿越，以支持欧盟移民政策制定。
摘要：
本文提出了一种混合方法，用于预测欧洲五条主要迁移动路线上的非法边境穿越，时间跨度为一年。该方法结合了机器学习技术与迁移专家的定性见解。这种方法旨在通过引入一个由人类评估的协变量来提高数据驱动模型的预测能力，以应对迁移模式突然变化和传统数据集限制带来的挑战。所提出的方法直接回应了欧盟移民与庇护协议中提出的预测需求，支持《庇护与迁移管理条例》（AMMR）。该方法旨在提供具有政策相关性的预测，以帮助战略决策、早期预警系统和欧盟成员国之间的团结机制。通过将数据驱动建模与专家判断相结合，本研究符合现有的学术建议，并引入了一种针对欧盟移民治理的新型操作工具。该方法已通过已知数据进行测试和验证，以展示其在移民相关政策环境中的适用性和可靠性。
英文摘要：
This paper presents a mixed-methodology to forecast illegal border crossings in Europe across five key migratory routes, with a one-year time horizon. The methodology integrates machine learning techniques with qualitative insights from migration experts. This approach aims at improving the predictive capacity of data-driven models through the inclusion of a human-assessed covariate, an innovation that addresses challenges posed by sudden shifts in migration patterns and limitations in traditional datasets. The proposed methodology responds directly to the forecasting needs outlined in the EU Pact on Migration and Asylum, supporting the Asylum and Migration Management Regulation (AMMR). It is designed to provide policy-relevant forecasts that inform strategic decisions, early warning systems, and solidarity mechanisms among EU Member States. By joining data-driven modeling with expert judgment, this work aligns with existing academic recommendations and introduces a novel operational tool tailored for EU migration governance. The methodology is tested and validated with known data to demonstrate its applicability and reliability in migration-related policy context.
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