国内移民决策的决策支持系统

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Boris Delibasic, S. Radovanović, S. Vukanovic
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引用次数: 0

摘要

本文提出了塞尔维亚共和国内部移民政策的决策支持系统,该系统使用机器学习和知识提取方法来分析数据并识别政策决策的关键特征。国内移徙是一个造成各国发展不平衡和可持续性挑战的问题。更具体地说,国内移徙给城市和城市地区带来了巨大压力,同时给后代留下了大量人口稀少和不可持续的城市化程度较低的地区。本文包括两个机器学习模型,用于预测地方自治政府(LSGs)的内部迁移强度,准确率为70%,以及提出的决策支持工具,准确率为66%。所建议的系统保持了决策支持系统所需的属性,如正确性、完整性、一致性、可理解性和便利性,并允许假设分析为每个LSG评估适当的策略。确定的关键特征可用于影响低人口群体的移民水平,促进塞尔维亚的平衡发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
IPSI BgD Transactions on Internet Research
IPSI BgD Transactions on Internet Research COMPUTER SCIENCE, INFORMATION SYSTEMS-
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