Solve the Refugee Crisis with Data

TL;DR

This paper develops and compares models for predicting refugee numbers and optimizing refugee allocation, introducing the Gary Verhulst Model and an AHP-based allocation scheme validated through rigorous testing.

Contribution

The study presents a novel combination of Gray Theory with logistic regression for refugee prediction and introduces a validated AHP-based model for refugee allocation.

Findings

Gary Verhulst Model outperforms logistic regression in prediction accuracy

AHP-based allocation scheme is validated as effective and scientific

Model comparisons suggest improvements over current schemes

Abstract

In this study, we addressed the refugee crisis through two main models. For predicting the ultimate number of refugees, we first established a Logistic Regression Model, but due to the limited data points, its prediction accuracy was suboptimal. Consequently, we incorporated Gray Theory to develop the Gary Verhulst Model, which provided scientifically sound and reasonable predictions. Statistical tests comparing both models highlighted the superiority of the Gary Verhulst Model. For formulating refugee allocation schemes, we initially used the Factor Analysis Method but found it too subjective and lacking in rigorous validation measures. We then developed a Refugee Allocation Model based on the Analytic Hierarchy Process (AHP), which absorbed the advantages of the former method. This model underwent extensive validation and passed consistency checks, resulting in an effective and scientific refugee allocation scheme. We also compared our model with the current allocation schemes and proposed improvements. Finally, we discussed the advantages and disadvantages of our models, their applicability, and scalability. Sensitivity analysis was conducted, and directions for future model improvements were identified.