A Decision Support System
For Internal Migration Policy-Making

This paper proposes a decision support system for internal migration policy in the Republic of Serbia, which uses machine learning and knowledge extraction methods to analyze data and identify key features for policy decision-making. Internal migration is an issue that creates uneven development and sustainability challenges in countries. More specifically, internal migrations are putting a big pressure on cities and urban areas, while leaving vast less-urbanized areas depopulated and unsustainable to future generations. This paper includes two machine learning models with an accuracy of 70% for predicting internal migration intensity in local selfgovernments (LSGs), as well as the proposed decision-support tool that achieves an accuracy of 66%. The proposed system maintains desirable properties of decision support systems such as correctness, completeness, consistency, comprehensibility, and convenience and allows the what-if analysis to evaluate appropriate policies for each LSG. The identified key features can be used to influence migration levels in LSGs and promote balanced development in Serbia.

Data engineering, DEX, Feature engineering, K-means clustering, knowledge extraction from databases, Machine learning.

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