A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case study
Anticipating future migration trends is instrumental to the development of effective policies to manage the challenges and opportunities that arise from population movements. However, anticipation is challenging. Migration is a complex system, with multifaceted drivers, such as demographic structure...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2024-01-01
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| Series: | Data & Policy |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2632324924000488/type/journal_article |
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| _version_ | 1846114577178165248 |
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| author | Claudio Bosco Umberto Minora Anna Rosińska Maurizio Teobaldelli Martina Belmonte |
| author_facet | Claudio Bosco Umberto Minora Anna Rosińska Maurizio Teobaldelli Martina Belmonte |
| author_sort | Claudio Bosco |
| collection | DOAJ |
| description | Anticipating future migration trends is instrumental to the development of effective policies to manage the challenges and opportunities that arise from population movements. However, anticipation is challenging. Migration is a complex system, with multifaceted drivers, such as demographic structure, economic disparities, political instability, and climate change. Measurements encompass inherent uncertainties, and the majority of migration theories are either under-specified or hardly actionable. Moreover, approaches for forecasting generally target specific migration flows, and this poses challenges for generalisation. |
| format | Article |
| id | doaj-art-3a6c51b61ff44fddb681c8bb3abe20fd |
| institution | Kabale University |
| issn | 2632-3249 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data & Policy |
| spelling | doaj-art-3a6c51b61ff44fddb681c8bb3abe20fd2024-12-20T09:06:35ZengCambridge University PressData & Policy2632-32492024-01-01610.1017/dap.2024.48A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case studyClaudio Bosco0https://orcid.org/0000-0002-6438-4571Umberto Minora1Anna Rosińska2https://orcid.org/0000-0003-4508-5565Maurizio Teobaldelli3Martina Belmonte4European Commission, Joint Research Centre (JRC), Ispra, ItalyEuropean Commission, Joint Research Centre (JRC), Ispra, ItalyEuropean Commission, Joint Research Centre (JRC), Ispra, ItalyARCADIA SIT S.R.L., Milano, ItalyEuropean Commission, Joint Research Centre (JRC), Ispra, ItalyAnticipating future migration trends is instrumental to the development of effective policies to manage the challenges and opportunities that arise from population movements. However, anticipation is challenging. Migration is a complex system, with multifaceted drivers, such as demographic structure, economic disparities, political instability, and climate change. Measurements encompass inherent uncertainties, and the majority of migration theories are either under-specified or hardly actionable. Moreover, approaches for forecasting generally target specific migration flows, and this poses challenges for generalisation.https://www.cambridge.org/core/product/identifier/S2632324924000488/type/journal_articleforecastingMachine Learningmigrationpolicy support |
| spellingShingle | Claudio Bosco Umberto Minora Anna Rosińska Maurizio Teobaldelli Martina Belmonte A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case study Data & Policy forecasting Machine Learning migration policy support |
| title | A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case study |
| title_full | A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case study |
| title_fullStr | A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case study |
| title_full_unstemmed | A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case study |
| title_short | A Machine Learning architecture to forecast Irregular Border Crossings and Asylum requests for policy support in Europe: a case study |
| title_sort | machine learning architecture to forecast irregular border crossings and asylum requests for policy support in europe a case study |
| topic | forecasting Machine Learning migration policy support |
| url | https://www.cambridge.org/core/product/identifier/S2632324924000488/type/journal_article |
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