Evaluation of atmospheric Poaceae pollen concentration using a neural network applied to a coastal Atlantic climate region

• Published in: Neural networks Vol. 23; no. 3; pp. 419 - 425
• Main Authors: Rodríguez-Rajo, F.J., Astray, G., Ferreiro-Lage, J.A., Aira, M.J., Jato-Rodriguez, M.V., Mejuto, J.C.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-04-2010; Elsevier
• Subjects: Aerobiology; Allergy; Anova; Applied sciences; Artificial intelligence; Atlantic Ocean; Atmospherics; Automation; Climate; Coastal; Computer science; control theory; systems; Computer Simulation; Connectionism. Neural networks; Exact sciences and technology; Learning theory; Linear Models; Mathematical models; Neural networks; Neural Networks (Computer); Poaceae; Pollen; Seasons; Spain; Time Factors; Atlantic Ocean; Spain; Allergy; Aerobiology; Poaceae; Pollen; Anova; Neural networks; Short term; Monocotyledones; Climate; Correlation; Modeling; Gramineae; Angiospermae; Automation; Statistical analysis; Probabilistic approach; Linear regression; Regression analysis; Neural network; Variance analysis; Long term; Forecasting; Spermatophyta; Aérobiology; Multilayer network
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2009.06.006

In the South of Europe an important percentage of population suffers pollen allergies, being the Poaceae pollen the major source. One of aerobiology’s objectives is to develop statistical models enabling the short- and long-term prediction of atmospheric pollen concentrations to take preventative measures to protect allergic patients from the severity of the atmospheric pollen season. The implementation of a computational model based on supervised MLP neural network was applied for the prediction of the atmospheric Poaceae pollen concentration. There is a good correlation between the values predicted by the ANN for the training cases in comparison with the real pollen concentrations. A high coefficient of linear regression
(
R
2
)
of 0.9696 was obtained. The accuracy of the neural network developed was tested with data from 2006 and 2007, which was not taken into account to establish the aforementioned models. Neural networks provided us a good tool to forecasting allergenic airborne pollen concentration helping the automation of the prediction system in the aerobiological information diffusion to the population suffering from allergic problems.