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A machine learning-based generalized approach for predicting unauthorized immigration flow considering dynamic border

Ridwan Al Aziz1, Tanvir Ahmed1, Jun Zhuang2

  • 1Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

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PubMed
Summary

Machine learning models can now predict unauthorized immigration flow, moving beyond localized surveys. The Bayesian Additive Regression Tree model demonstrated the best predictive performance in this novel approach.

Keywords:
Bayesian Additive Regression Treeclimate changecross‐borderillegal border crossinginternational migration

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Area of Science:

  • Social Sciences
  • Computer Science
  • Data Science

Background:

  • Unauthorized immigration is a complex global challenge driven by economic, social, and environmental factors.
  • Events like pandemics exacerbate migration instability, necessitating advanced analytical methods.
  • Traditional survey-based approaches are limited in scope and scalability for analyzing migration dynamics.

Purpose of the Study:

  • To introduce a novel, nonparametric machine learning framework for analyzing unauthorized immigration.
  • To develop a comprehensive database independent of localized surveys for migration analysis.
  • To compare the predictive performance of nine nonparametric machine learning algorithms.

Main Methods:

  • Deployment of nine nonparametric machine learning algorithms to predict unauthorized immigration flow.
  • Utilizing a framework that incorporates the dynamic border security nexus.
  • Establishing the Seasonal Autoregressive Integrated Moving Average (SARIMA) model as the null model for comparison.

Main Results:

  • The Bayesian Additive Regression Tree (BART) model emerged as the best-performing algorithm for predicting unauthorized immigration.
  • The proposed machine learning framework offers a cost-effective, faster, and big data-friendly alternative to traditional methods.
  • The study successfully created a comprehensive database independent of localized surveys.

Conclusions:

  • Nonparametric machine learning approaches provide a powerful tool for understanding and predicting unauthorized immigration.
  • The developed framework offers significant advantages in terms of efficiency and scalability for migration analysis.
  • Future research should leverage these advanced methods to address the complexities of global migration patterns.