
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…
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Taxonomy
TopicsAsian Geopolitics and Ethnography
