{"title":"国内移民决策的决策支持系统","authors":"Boris Delibasic, S. Radovanović, S. Vukanovic","doi":"10.58245/ipsi.tir.2302.07","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":41192,"journal":{"name":"IPSI BgD Transactions on Internet Research","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Decision Support System for Internal Migration Policy-Making\",\"authors\":\"Boris Delibasic, S. Radovanović, S. Vukanovic\",\"doi\":\"10.58245/ipsi.tir.2302.07\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":41192,\"journal\":{\"name\":\"IPSI BgD Transactions on Internet Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IPSI BgD Transactions on Internet Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.58245/ipsi.tir.2302.07\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IPSI BgD Transactions on Internet Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.58245/ipsi.tir.2302.07","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.