title: Artificial neural network model of the relationship between Betula pollen and meteorological factors in Szczecin (Poland).
abstract: 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.
authors.affiliation: Department of Botany and Nature Conservation, University of Szczecin, Felczaka 3c, 71-412, Szczecin, Poland. mapuc@univ.szczecin.pl
authors.forename: Małgorzata
authors.initials: M
authors.lastname: Puc
mesh_terms: D000485:Allergens; D029662:Betula / Q000502:physiology*; D005544:Forecasting / Q000379:methods; D016571:Neural Networks, Computer; D011044:Poland; D011058:Pollen; D014887:Weather
chemical_list: D000485:Allergens