Supporting Migration Policies with Forecasts: Illegal Border Crossings in Europe through a Mixed Approach

TL;DR

This paper introduces a mixed-methodology combining machine learning and expert insights to forecast illegal border crossings in Europe, aiming to enhance policy decision-making and early warning systems.

Contribution

It presents a novel hybrid approach that integrates data-driven models with qualitative expert input for improved migration forecasting.

Findings

The methodology effectively predicts migration flows across five key routes.

Inclusion of expert judgment improves model adaptability to sudden migration shifts.

Validated approach demonstrates reliability for policy support in EU migration management.

Abstract

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.