Unauthorized immigration has been a long-standing and contentious challenge for developed and developing countries. Numerous continually evolving push and pull factors across international borders, such as economy, employment, population density, unrest, corruption, and climate have driven this migration. Large-scale pandemics such as COVID-19, causing further instability in countries' financial well-being, can initiate or alter emigration flow from different countries. In light of such a complex confluence of factors, climate change, and demographic shifts in migrant communities, it is high time to shift toward machine learning-reinforced generalized approaches from the traditional parametric approaches based on migrant community-specific localized surveys. To our best knowledge, no literature has explored the nonparametric approach and developed a comprehensive database independent of localized surveys to analyze unauthorized migration. This article fills this gap by deploying nine nonparametric machine learning algorithms for predicting unauthorized immigration flow considering the dynamic border security nexus. This framework considers the Seasonal Autoregressive Integrated Moving Average model as the null model. The proposed novel framework removes the dependency on localized survey-based studies and provides a more cost-effective, faster, and big data-friendly approach. This study finds the Bayesian Additive Regression Tree model as the best predictive model.
Keywords: Bayesian Additive Regression Tree; climate change; cross-border; illegal border crossing; international migration.
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