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Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count
The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus, Alnus, and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during e...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996891/ https://www.ncbi.nlm.nih.gov/pubmed/27616811 http://dx.doi.org/10.1007/s10453-015-9418-y |
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author | Nowosad, Jakub Stach, Alfred Kasprzyk, Idalia Weryszko-Chmielewska, Elżbieta Piotrowska-Weryszko, Krystyna Puc, Małgorzata Grewling, Łukasz Pędziszewska, Anna Uruska, Agnieszka Myszkowska, Dorota Chłopek, Kazimiera Majkowska-Wojciechowska, Barbara |
author_facet | Nowosad, Jakub Stach, Alfred Kasprzyk, Idalia Weryszko-Chmielewska, Elżbieta Piotrowska-Weryszko, Krystyna Puc, Małgorzata Grewling, Łukasz Pędziszewska, Anna Uruska, Agnieszka Myszkowska, Dorota Chłopek, Kazimiera Majkowska-Wojciechowska, Barbara |
author_sort | Nowosad, Jakub |
collection | PubMed |
description | The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus, Alnus, and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split. For each taxon and city, the model was built using a random forest method. Corylus models performed poorly. However, the study revealed the possibility of predicting with substantial accuracy the occurrence of days with high pollen concentrations of Alnus and Betula using past pollen count data from monitoring sites. These results can be used for building (1) simpler models, which require data only from aerobiological monitoring sites, and (2) combined meteorological and aerobiological models for predicting high levels of pollen concentration. |
format | Online Article Text |
id | pubmed-4996891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-49968912016-09-08 Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count Nowosad, Jakub Stach, Alfred Kasprzyk, Idalia Weryszko-Chmielewska, Elżbieta Piotrowska-Weryszko, Krystyna Puc, Małgorzata Grewling, Łukasz Pędziszewska, Anna Uruska, Agnieszka Myszkowska, Dorota Chłopek, Kazimiera Majkowska-Wojciechowska, Barbara Aerobiologia (Bologna) OriginalPaper The aim of the study was to create and evaluate models for predicting high levels of daily pollen concentration of Corylus, Alnus, and Betula using a spatiotemporal correlation of pollen count. For each taxon, a high pollen count level was established according to the first allergy symptoms during exposure. The dataset was divided into a training set and a test set, using a stratified random split. For each taxon and city, the model was built using a random forest method. Corylus models performed poorly. However, the study revealed the possibility of predicting with substantial accuracy the occurrence of days with high pollen concentrations of Alnus and Betula using past pollen count data from monitoring sites. These results can be used for building (1) simpler models, which require data only from aerobiological monitoring sites, and (2) combined meteorological and aerobiological models for predicting high levels of pollen concentration. Springer Netherlands 2015-12-14 2016 /pmc/articles/PMC4996891/ /pubmed/27616811 http://dx.doi.org/10.1007/s10453-015-9418-y Text en © The Author(s) 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | OriginalPaper Nowosad, Jakub Stach, Alfred Kasprzyk, Idalia Weryszko-Chmielewska, Elżbieta Piotrowska-Weryszko, Krystyna Puc, Małgorzata Grewling, Łukasz Pędziszewska, Anna Uruska, Agnieszka Myszkowska, Dorota Chłopek, Kazimiera Majkowska-Wojciechowska, Barbara Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count |
title | Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count |
title_full | Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count |
title_fullStr | Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count |
title_full_unstemmed | Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count |
title_short | Forecasting model of Corylus, Alnus, and Betula pollen concentration levels using spatiotemporal correlation properties of pollen count |
title_sort | forecasting model of corylus, alnus, and betula pollen concentration levels using spatiotemporal correlation properties of pollen count |
topic | OriginalPaper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996891/ https://www.ncbi.nlm.nih.gov/pubmed/27616811 http://dx.doi.org/10.1007/s10453-015-9418-y |
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