Department of Botany and Nature Conservation, University of Szczecin, Felczaka 3c, 71-412, Szczecin, Poland.
Int J Biometeorol. 2012 Mar;56(2):395-401. doi: 10.1007/s00484-011-0446-1. Epub 2011 May 15.
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.
桦树花粉是北欧和中欧春季和初夏过敏的主要原因之一。本研究旨在创建一个预测模型，能够准确预测波兰什切青大气中桦属花粉粒的日平均浓度。为了实现这一目标，使用了一种新的数据分析技术——人工神经网络（ANN）。2003-2009 年期间，在什切青使用了赫氏体积孢子捕捉器进行采样。Spearman 等级相关分析显示，湿度与桦树花粉浓度呈强负相关。最高温度、平均温度、最低温度和降水与桦树花粉浓度呈显著正相关。人工神经网络得到了多层感知器 3668:2928-7-1:1，时间序列预测具有相当高的准确性（SD 比值在 0.3 到 0.5 之间，R > 0.85）。观察值和计算值的直接比较证实了模型的良好性能及其能够再现大部分变化的能力。
