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

Abstract We introduce 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.