Use of neural networks to short-term forecast of airborne pollen data (Contributo in atti di convegno)

Type
Label
  • Use of neural networks to short-term forecast of airborne pollen data (Contributo in atti di convegno) (literal)
Anno
  • 2004-01-01T00:00:00+01:00 (literal)
Alternative label
  • Arca B., Pellizzaro G., Canu A., Vargiu G. (2004)
    Use of neural networks to short-term forecast of airborne pollen data
    in 16th Conference on Biometeorology and Aerobiology, Vancouver, Canada
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Arca B., Pellizzaro G., Canu A., Vargiu G. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • Abstract: In the last two decades the allergic diseases inducted by allergenic pollen have dramatically increased. A better application of preventive allergic therapy is highly dependent on improvements of the methods for early forecasting of pollen data. Analytical models, which are based on differential equations, describe emission and dispersion of pollen in the atmosphere and, therefore, can be use to forecast pollen data. These models combine many parameters related to plant observations and weather conditions, but their application is difficult because of lack of data with regard to parameters. An alternative way to forecast pollen data is based on the statistical analysis of pollen concentrations, by means of time series methods. The aims of this study are (I) to develop a neural network model to short-term forecast pollen concentration and (II) to analyse the effect on the forecasted values of the different model parameters and (III) to improve the accuracy of airborne pollen forecasting for some of the main allergenic plants of the Mediterranean area. The analysis was performed on Graminaceae and Oleaceae. All the ANNs use as input the calibration airborne pollen time series; the forecasting period ranged between 1 to 7 days. Subsequent data were forecasted using both one-lag and multi-lag methodologies. The study shows the capabilities of ANN as a tool for short-term forecasting of pollen concentration and as a support for application preventive allergic therapy. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Vargiu A.: Osservatorio aerobiologico di Sassari. (literal)
Titolo
  • Use of neural networks to short-term forecast of airborne pollen data (literal)
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