Forecasting daily total pollen concentrations on a global scale

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dc.authorid0000-0001-7424-8963en_US
dc.authorid0000-0002-2705-5728en_US
dc.authorid0000-0002-5384-3605en_US
dc.authorid0000-0002-8660-5451en_US
dc.contributor.authorMakra, László
dc.contributor.authorCoviello, Luca
dc.contributor.authorGobbi, Andrea
dc.contributor.authorJurman, Giuseppe
dc.contributor.authorFurlanello, Cesare
dc.contributor.authorBrunato, Mauro
dc.contributor.authorÖzaslan, Cumali
dc.date.accessioned2024-09-06T11:19:36Z
dc.date.available2024-09-06T11:19:36Z
dc.date.issued2024en_US
dc.departmentDicle Üniversitesi, Ziraat Fakültesi, Bitki Koruma Bölümüen_US
dc.description.abstractBackground: There is evidence that global anthropogenic climate change may be impacting floral phenology and the temporal and spatial characteristics of aero-allergenic pollen. Given the extent of current and future climate uncertainty, there is a need to strengthen predictive pollen forecasts. Methods: The study aims to use CatBoost (CB) and deep learning (DL) models for predicting the daily total pollen concentration up to 14 days in advance for 23 cities, covering all five continents. The model includes the projected environmental parameters, recent concentrations (1, 2 and 4 weeks), and the past environmental explanatory variables, and their future values. Results: The best pollen forecasts include Mexico City (R2(DL_7) ≈.7), and Santiago (R2(DL_7) ≈.8) for the 7th forecast day, respectively; while the weakest pollen forecasts are made for Brisbane (R2(DL_7) ≈.4) and Seoul (R2(DL_7) ≈.1) for the 7th forecast day. The global order of the five most important environmental variables in determining the daily total pollen concentrations is, in decreasing order: the past daily total pollen concentration, future 2 m temperature, past 2 m temperature, past soil temperature in 28–100 cm depth, and past soil temperature in 0–7 cm depth. City-related clusters of the most similar distribution of feature importance values of the environmental variables only slightly change on consecutive forecast days for Caxias do Sul, Cape Town, Brisbane, and Mexico City, while they often change for Sydney, Santiago, and Busan. Conclusions: This new knowledge of the ecological relationships of the most remarkable variables importance for pollen forecast models according to clusters, cities and forecast days is important for developing and improving the accuracy of airborne pollen forecasts.en_US
dc.identifier.citationMakra, L., Coviello, L., Gobbi, A., Jurman, G., Furlanello, C., Brunato, M. ve diğerleri. (2024). Forecasting daily total pollen concentrations on a global scale. Allergy: European Journal of Allergy and Clinical Immunology, 78(8), 2173-2185.en_US
dc.identifier.endpage2185en_US
dc.identifier.issn0105-4538
dc.identifier.issue8en_US
dc.identifier.pmid38995241
dc.identifier.scopus2-s2.0-85198521687
dc.identifier.scopusqualityQ1
dc.identifier.startpage2173en_US
dc.identifier.urihttps://onlinelibrary.wiley.com/doi/epdf/10.1111/all.16227
dc.identifier.urihttps://hdl.handle.net/11468/28815
dc.identifier.volume78en_US
dc.identifier.wosWOS:001281396400012
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakPubMed
dc.indekslendigikaynakScopus
dc.institutionauthorÖzaslan, Cumali
dc.language.isoenen_US
dc.publisherJohn Wiley and Sons Inc.en_US
dc.relation.ispartofAllergy: European Journal of Allergy and Clinical Immunology
dc.relation.isversionof10.1111/all.16227en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAllergyen_US
dc.subjectArtificial intelligenceen_US
dc.subjectEnvironmental variablesen_US
dc.subjectFeature importance clusteren_US
dc.subjectPollen forecasten_US
dc.titleForecasting daily total pollen concentrations on a global scaleen_US
dc.titleForecasting daily total pollen concentrations on a global scale
dc.typeArticleen_US

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