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The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region
Aerobiologia ( IF 2 ) Pub Date : 2019-11-09 , DOI: 10.1007/s10453-019-09615-w
Z. Csépe , Á. Leelőssy , G. Mányoki , D. Kajtor-Apatini , O. Udvardy , B. Péter , A. Páldy , G. Gelybó , T. Szigeti , T. Pándics , A. Kofol-Seliger , A. Simčič , P. M. Leru , A.-M. Eftimie , B. Šikoparija , P. Radišić , B. Stjepanović , I. Hrga , A. Večenaj , A. Vucić , D. Peroš-Pucar , T. Škorić , J. Ščevková , M. Bastl , U. Berger , D. Magyar

Ragweed Pollen Alarm System (R-PAS) has been running since 2014 to provide pollen information for countries in the Pannonian biogeographical region (PBR). The aim of this study was to develop forecast models of the representative aerobiological monitoring stations, identified by analysis based on a neural network computation. Monitoring stations with 7-day Hirst-type pollen trap having 10-year long validated data set of ragweed pollen were selected for the study from the PBR. Variables including forecasted meteorological data, pollen data of the previous days and nearby monitoring stations were used as input of the model. We used the multilayer perceptron model to forecast the pollen concentration. The multilayer perceptron (MLP) is a feedforward artificial neural network. MLP is a data-driven method to forecast the behaviour of complex systems. In our case, it has three layers, one of which is hidden. MLP utilizes a supervised learning technique called backpropagation for training to get better performance. By testing the neural network, we selected different sets of variables to predict pollen levels for the next 3 days in each of the monitoring stations. The predicted pollen level categories (low–medium–high–very high) are shown on isarithmic map. We used the mean square error, mean absolute error and correlation coefficient metrics to show the forecasting system’s performance. The average of the Pearson correlations is around 0.6 but shows big variability (0.13–0.88) among different locations. Model uncertainty is mainly caused by the limitation of the available input data and the variability in ragweed season patterns. Visualization of the results of the neural network forecast on isarithmic maps is a good tool to communicate pollen information to general public in the PBR.

中文翻译:

豚草花粉报警系统基于神经网络的豚草花粉预测在潘诺尼亚生物地理区域的应用

豚草花粉警报系统 (R-PAS) 自 2014 年开始运行,为潘诺尼亚生物地理区域 (PBR) 的国家提供花粉信息。本研究的目的是开发代表性空气生物监测站的预测模型,通过基于神经网络计算的分析确定。从 PBR 中选择了具有 7 天 Hirst 型花粉陷阱的监测站,该站具有 10 年的豚草花粉验证数据集。模型的输入变量包括预报的气象数据、前几天的花粉数据和附近的监测站。我们使用多层感知器模型来预测花粉浓度。多层感知器(MLP)是一种前馈人工神经网络。MLP 是一种数据驱动的方法,用于预测复杂系统的行为。在我们的例子中,它有三层,其中一层是隐藏的。MLP 利用称为反向传播的监督学习技术进行训练以获得更好的性能。通过测试神经网络,我们选择了不同的变量集来预测每个监测站接下来 3 天的花粉水平。预测的花粉水平类别(低 - 中 - 高 - 非常高)显示在等距图上。我们使用均方误差、平均绝对误差和相关系数指标来显示预测系统的性能。Pearson 相关系数的平均值约为 0.6,但在不同位置之间显示出很大的变异性 (0.13–0.88)。模型的不确定性主要是由可用输入数据的限制和豚草季节模式的可变性引起的。
更新日期:2019-11-09
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