Statistical techniques for modeling of Corylus, Alnus, and Betula pollen concentration in the air

Jakub Nowosad , Alfred Stach , Idalia Kasprzyk , Kazimiera Chłopek , Katarzyna Dąbrowska-Zapart , Łukasz Grewling , Małgorzata Latałowa , Anna Pędziszewska , Barbara Majkowska-Wojciechowska , Dorota Myszkowska , Krystyna Piotrowska-Weryszko , Elżbieta Weryszko-Chmielewska , Małgorzata Puc , Piotr Rapiejko , Tomasz Stosik

Abstract

Prediction of allergic pollen concentration is one of the most important goals of aerobiology. Past studies have used a broad range of modeling techniques; however, the results cannot be directly compared owing to the use of different datasets, validation methods, and evaluation metrics. The main aim of this study was to compare nine statistical modeling techniques using the same dataset. An additional goal was to assess the importance of predictors for the best model. Aerobiological data for Corylus, Alnus, and Betula pollen counts were obtained from nine cities in Poland and covered between five and 16 years of measurements. Meteorological data from the AGRI4- CAST project were used as a predictor variables. The results of 243 final models (3 taxa x 9 cities x 9 techniques) were validated using a repeated k-fold cross-validation and compared using relative and absolute performance statistics. Afterward, the variable importance of predictors in the best models was calculated and compared. Simple models performed poorly. On the other hand, regression trees and rulebased models proved to be the most accurate for all of the taxa. Cumulative growing degree days proved to be the single most important predictor variable in the random forest models of Corylus, Alnus, and Betula. Finally, the study suggested potential improvements in aerobiological modeling, such as the application of robust cross-validation techniques and the use of gridded variables.
Author Jakub Nowosad
Jakub Nowosad,,
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, Alfred Stach
Alfred Stach,,
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, Idalia Kasprzyk
Idalia Kasprzyk,,
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, Kazimiera Chłopek
Kazimiera Chłopek,,
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, Katarzyna Dąbrowska-Zapart
Katarzyna Dąbrowska-Zapart,,
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, Łukasz Grewling
Łukasz Grewling,,
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, Małgorzata Latałowa (FB / DPE)
Małgorzata Latałowa,,
- Department of Plant Ecolog
, Anna Pędziszewska (FB / DPE)
Anna Pędziszewska,,
- Department of Plant Ecolog
, Barbara Majkowska-Wojciechowska
Barbara Majkowska-Wojciechowska,,
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, Dorota Myszkowska
Dorota Myszkowska,,
-
et al.`
Journal seriesAerobiologia, ISSN 0393-5965, (A 25 pkt)
Issue year2018
Vol34
No3
Pages301-313
Publication size in sheets0.6
Keywords in EnglishAllergenic pollen, pollen concentration in the air, Betulaceae, regression models, predictive modeling, machine learning
DOIDOI:10.1007/s10453-018-9514-x
URL https://doi.org/10.1007/s10453-018-9514-x
Languageen angielski
Score (nominal)25
ScoreMinisterial score = 25.0, 25-10-2018, ArticleFromJournal
Ministerial score (2013-2016) = 25.0, 25-10-2018, ArticleFromJournal
Publication indicators WoS Impact Factor: 2016 = 2.202 (2) - 2016=1.939 (5)
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