Modeling Climate-Induced Refugee Migration: An Explainable Machine Learning Approach

Haodong Qi, Malmo University
Alina Sîrbu, University of Pisa
Rahman Momeni, GMV
Enes Hisam, GMV
Carlos Arcila-Calderón, University of Salamanca
Tuba Bircan, Free University of Brussels
Stefano Iacus, Harvard University

This paper introduces a novel machine learning model to explain and predict climate-induced refugee migration. Through a case study of Somalis seeking asylum in the EU, we demonstrated three key features of our approach. First, by combining lead-lag analysis and Elastic Net regularization, our model can efficiently extract important predictors and their optimal lags from a high-dimensional space containing thousands of location-specific indicators within a country of origin. This feature is useful for identifying where and when migration responses to climate conditions are likely to occur. Moreover, by training on rolling time windows, our model can analyze the persistence of migration drivers and examine whether persistent drivers might induce qualitative (abrupt) changes in the functioning of migration systems. Finally, compared to common time series extrapolation methods (auto-regressive models), our approach can deliver more accurate forecasts and more reliable assessment of uncertainties. In this regard, our machine learning approach is proven to be not only predictive, but also explainable.

Keywords: Population, Environment, and Climate Change, International Migration, Big data, Population projections, forecasts, and estimations

See paper.