Birch pollen is one of the main causes of allergy during spring and early summer in northern and central Europe. The aim of this study was to create a forecast model that can accurately predict daily average concentrations of Betula sp. pollen grains in the atmosphere of Szczecin, Poland. In order to achieve this, a novel data analysis technique—artificial neural networks (ANN)—was used. Sampling was carried out using a volumetric spore trap of the Hirst design in Szczecin during 2003–2009. Spearman’s rank correlation analysis revealed that humidity had a strong negative correlation with Betula pollen concentrations. Significant positive correlations were observed for maximum temperature, average temperature, minimum temperature and precipitation. The ANN resulted in multilayer perceptrons 366 8: 2928-7-1:1, time series prediction was of quite high accuracy (SD Ratio between 0.3 and 0.5, R > 0.85). Direct comparison of the observed and calculated values confirmed good performance of the model and its ability to recreate most of the variation.
Data udostępnienia | 6 mar 2023, 09:57:23 |
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Data mod. | 6 mar 2023, 09:57:23 |
Dostęp | Publiczny |
Aktywnych wyświetleń | 0 |