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Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey
In this study, forecasting the number of immigrants on the Turkey’s maritime line for use in a national security project carried out by Turkish Government within the scope of fight against uncontrolled immigration is discussed for the first time. Handling with the immigration problem is one of the b...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9815689/ https://www.ncbi.nlm.nih.gov/pubmed/36628119 http://dx.doi.org/10.1007/s00500-022-07800-7 |
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author | Cevik, Fatma Carman Gever, Basak Tak, Nihat Khaniyev, Tahir |
author_facet | Cevik, Fatma Carman Gever, Basak Tak, Nihat Khaniyev, Tahir |
author_sort | Cevik, Fatma Carman |
collection | PubMed |
description | In this study, forecasting the number of immigrants on the Turkey’s maritime line for use in a national security project carried out by Turkish Government within the scope of fight against uncontrolled immigration is discussed for the first time. Handling with the immigration problem is one of the biggest concerns of Turkey as unsupervised immigration can adversely affect the demographic and economic structure of the country. Precautions are needed as the short-, medium- and long-term impacts of undetected immigrants on the country’s ecosystem are unpredictable, but due to the uncertainties inherent in immigration, the cost of using government resources such as patrol vehicles to capture undocumented immigrants can be extremely high. In order to both minimize the expenditure problem and keep immigration under control by providing a proper scan, forecasting the number of immigrants on the maritime line route is seen as an important problem and studied by probabilistic and non-probabilistic models. Since the data for 2020 and 2021 could not be attained yet due to COVID-19, in order to obtain forecasts and compare actual observations for 2019, which is the primarily focus of the research in this study, the dataset of interest on the number of daily immigrants between years 2016 and 2019 is obtained from Turkish Coast Guard Command within Ministry of Interior of Republic of Turkey. To obtain the most accurate forecasts, seven distinguished forecasting methods, from simple to complex, are implemented. Then, the forecast combination approach with meta-fuzzy functions which combines all methods is proposed. Consequently, the forecasting results are acquired and evaluated by using R. The evaluation of the results is made by using widely considered measurement accuracy metric root mean square error. According to the final assessments, the proposed approach gives more accurate forecasting results for the expected number of immigrants on the Turkey’s maritime line and these results become an input to the national security project. |
format | Online Article Text |
id | pubmed-9815689 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-98156892023-01-06 Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey Cevik, Fatma Carman Gever, Basak Tak, Nihat Khaniyev, Tahir Soft comput Mathematical Methods in Data Science In this study, forecasting the number of immigrants on the Turkey’s maritime line for use in a national security project carried out by Turkish Government within the scope of fight against uncontrolled immigration is discussed for the first time. Handling with the immigration problem is one of the biggest concerns of Turkey as unsupervised immigration can adversely affect the demographic and economic structure of the country. Precautions are needed as the short-, medium- and long-term impacts of undetected immigrants on the country’s ecosystem are unpredictable, but due to the uncertainties inherent in immigration, the cost of using government resources such as patrol vehicles to capture undocumented immigrants can be extremely high. In order to both minimize the expenditure problem and keep immigration under control by providing a proper scan, forecasting the number of immigrants on the maritime line route is seen as an important problem and studied by probabilistic and non-probabilistic models. Since the data for 2020 and 2021 could not be attained yet due to COVID-19, in order to obtain forecasts and compare actual observations for 2019, which is the primarily focus of the research in this study, the dataset of interest on the number of daily immigrants between years 2016 and 2019 is obtained from Turkish Coast Guard Command within Ministry of Interior of Republic of Turkey. To obtain the most accurate forecasts, seven distinguished forecasting methods, from simple to complex, are implemented. Then, the forecast combination approach with meta-fuzzy functions which combines all methods is proposed. Consequently, the forecasting results are acquired and evaluated by using R. The evaluation of the results is made by using widely considered measurement accuracy metric root mean square error. According to the final assessments, the proposed approach gives more accurate forecasting results for the expected number of immigrants on the Turkey’s maritime line and these results become an input to the national security project. Springer Berlin Heidelberg 2023-01-05 2023 /pmc/articles/PMC9815689/ /pubmed/36628119 http://dx.doi.org/10.1007/s00500-022-07800-7 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Mathematical Methods in Data Science Cevik, Fatma Carman Gever, Basak Tak, Nihat Khaniyev, Tahir Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey |
title | Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey |
title_full | Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey |
title_fullStr | Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey |
title_full_unstemmed | Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey |
title_short | Forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in Turkey |
title_sort | forecast combination approach with meta-fuzzy functions for forecasting the number of immigrants within the maritime line security project in turkey |
topic | Mathematical Methods in Data Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9815689/ https://www.ncbi.nlm.nih.gov/pubmed/36628119 http://dx.doi.org/10.1007/s00500-022-07800-7 |
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