Machine Learning Regression to Predict Pollen Concentrations of Oleaceae and Quercus Taxa in Thessaloniki, Greece
Airborne pollen triggers allergic reactions in up to 40% of the global population. The incidence of pollen allergies is increasing in Thessaloniki, Greece and it is predicted that more than 50% of the European Union’s inhabitants will suffer from allergic rhinitis by 2025. Thus, it is essential to i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Environmental Sciences Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4931/26/1/2 |
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| author | Sofia Papadogiannaki Serafeim Kontos Daphne Parliari Dimitrios Melas |
| author_facet | Sofia Papadogiannaki Serafeim Kontos Daphne Parliari Dimitrios Melas |
| author_sort | Sofia Papadogiannaki |
| collection | DOAJ |
| description | Airborne pollen triggers allergic reactions in up to 40% of the global population. The incidence of pollen allergies is increasing in Thessaloniki, Greece and it is predicted that more than 50% of the European Union’s inhabitants will suffer from allergic rhinitis by 2025. Thus, it is essential to investigate and predict high pollen concentrations to address this growing concern. This study utilized the Gradient Boosting Regression (GBR) technique, a machine learning approach, to estimate pollen concentrations of Oleaceae and Quercus taxa, using daily meteorological and land surface data obtained from the European Center for Medium-Range Weather Forecasts (ECMWF). The method accurately predicted pollen concentrations for both species, with an Index of Agreement (IoA) of 0.86 for Oleaceae and 0.78 for Quercus, despite the limited size of the dataset. |
| first_indexed | 2024-04-24T18:18:59Z |
| format | Article |
| id | doaj.art-3c2f509996974c7ea14eed94912edb73 |
| institution | Directory Open Access Journal |
| issn | 2673-4931 |
| language | English |
| last_indexed | 2024-04-24T18:18:59Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Environmental Sciences Proceedings |
| spelling | doaj.art-3c2f509996974c7ea14eed94912edb732024-03-27T13:37:08ZengMDPI AGEnvironmental Sciences Proceedings2673-49312023-08-01261210.3390/environsciproc2023026002Machine Learning Regression to Predict Pollen Concentrations of Oleaceae and Quercus Taxa in Thessaloniki, GreeceSofia Papadogiannaki0Serafeim Kontos1Daphne Parliari2Dimitrios Melas3Laboratory of Atmospheric Physics, School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceLaboratory of Atmospheric Physics, School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceLaboratory of Atmospheric Physics, School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceLaboratory of Atmospheric Physics, School of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceAirborne pollen triggers allergic reactions in up to 40% of the global population. The incidence of pollen allergies is increasing in Thessaloniki, Greece and it is predicted that more than 50% of the European Union’s inhabitants will suffer from allergic rhinitis by 2025. Thus, it is essential to investigate and predict high pollen concentrations to address this growing concern. This study utilized the Gradient Boosting Regression (GBR) technique, a machine learning approach, to estimate pollen concentrations of Oleaceae and Quercus taxa, using daily meteorological and land surface data obtained from the European Center for Medium-Range Weather Forecasts (ECMWF). The method accurately predicted pollen concentrations for both species, with an Index of Agreement (IoA) of 0.86 for Oleaceae and 0.78 for Quercus, despite the limited size of the dataset.https://www.mdpi.com/2673-4931/26/1/2airborne pollenpollen concentrationsOleaceaeQuercusmachine learningGradient Boosting Regression |
| spellingShingle | Sofia Papadogiannaki Serafeim Kontos Daphne Parliari Dimitrios Melas Machine Learning Regression to Predict Pollen Concentrations of Oleaceae and Quercus Taxa in Thessaloniki, Greece Environmental Sciences Proceedings airborne pollen pollen concentrations Oleaceae Quercus machine learning Gradient Boosting Regression |
| title | Machine Learning Regression to Predict Pollen Concentrations of Oleaceae and Quercus Taxa in Thessaloniki, Greece |
| title_full | Machine Learning Regression to Predict Pollen Concentrations of Oleaceae and Quercus Taxa in Thessaloniki, Greece |
| title_fullStr | Machine Learning Regression to Predict Pollen Concentrations of Oleaceae and Quercus Taxa in Thessaloniki, Greece |
| title_full_unstemmed | Machine Learning Regression to Predict Pollen Concentrations of Oleaceae and Quercus Taxa in Thessaloniki, Greece |
| title_short | Machine Learning Regression to Predict Pollen Concentrations of Oleaceae and Quercus Taxa in Thessaloniki, Greece |
| title_sort | machine learning regression to predict pollen concentrations of oleaceae and quercus taxa in thessaloniki greece |
| topic | airborne pollen pollen concentrations Oleaceae Quercus machine learning Gradient Boosting Regression |
| url | https://www.mdpi.com/2673-4931/26/1/2 |
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