融合机器学习与专家判断，提升欧洲非法越境预测精度。
Supporting Migration Policies with Forecasts: Illegal Border Crossings in Europe through a Mixed Approach
- 结合算法模型与移民专家经验，引入人为评估变量。
- 在五年主要路线中实现一年期预测，提升突发迁移应对能力。
- 专为欧盟移民治理设计，适合政策制定与预警系统使用。
本文提出一种混合方法，用于预测欧洲五大关键迁徙路线上一年内的非法边境越境情况。该方法融合机器学习技术与移民专家的定性洞察，通过引入人工评估的协变量，增强数据驱动模型的预测能力，以应对迁移模式突变及传统数据集的局限性。该方法直接回应欧盟《移民与庇护协定》提出的预测需求，支持《庇护与移民管理条例》（AMMR），旨在提供具有政策意义的预测结果，助力战略决策、早期预警系统以及欧盟成员国间的团结机制。通过已知数据验证，证明其在移民政策环境中的适用性与可靠性。
原文摘要 · Abstract (English)
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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