Thursday 17 December 2015

Forecasting Olea Airborne Pollen Concentrations by Means of Artificial Intelligence

Fresenius Environmental Bulletin, (2015) 24, 4574-4580

A model based in an Artificial Neural Network was de- veloped in order to forecast the Olea airborne pollen concen- tration due to the allergenic power of its pollen grains. Daily data for Olea pollen and the meteorological variables docu- mented in the period 1993-2008, were used. All developed models had five input variables; i) Julian day, ii) precipita- tion, iii) humidity value, and iv) insolation hours. The model was carried out with data collected in the city of Ourense, North West of Spain. The developed model predicts the at- mospheric concentration of Olea pollen one day ahead. The model was tested with data from 2009 and 2010. The devel- oped model could be employed in allergology and health sci- ences in order to prevent effect of pollinosis. It is due the ability of obtain good predictions of threshold pollen con- centration values, which are important to take preventive measures by Health Systems. The time-lag observed in pre- diction phase may be due to the influence of other meteoro- logical parameters which have not been taken into account in the beginning, this fact is reaffirmed by studying the simi- larity between the different pollen seasons and the year 2010 (Mann-Whitney U Test with p<0.031), or due the low concen- trations of Olea pollen during the MPS (≈ 14 grains·m-3).