Allergy Asthma Immunol Res.  2020 Jan;12(1):149-163. 10.4168/aair.2020.12.1.149.

Deep Neural Network-Based Concentration Model for Oak Pollen Allergy Warning in South Korea

Affiliations
  • 1AI Weather Forecast Research Team, National Institute of Meteorological Science, Seogwipo, Korea.
  • 2Applied Meteorology Research Division, National Institute of Meteorological Science, Seogwipo, Korea. krk9@kma.go.kr
  • 3Department of Pediatrics, Hanyang University College of Medicine, Seoul, Korea.
  • 4Urban Forest Research Center, National Institute of Forest Science, Korea Forest Service, Seoul, Korea.

Abstract

PURPOSE
Oak is the dominant tree species in Korea. Oak pollen has the highest sensitivity rate among all allergenic tree species in Korea. A deep neural network (DNN)-based estimation model was developed to determine the concentration of oak pollen and overcome the shortcomings of conventional regression models.
METHODS
The DNN model proposed in this study utilized weather factors as the input and provided pollen concentrations as the output. Weather and pollen concentration data were used from 2007 to 2016 obtained from the Korea Meteorological Administration pollen observation network. Because it is difficult to prevent over-fitting and underestimation by using a DNN model alone, we developed a bootstrap aggregating-type ensemble model. Each of the 30 ensemble members was trained with random sampling at a fixed rate according to the pollen risk grade. To verify the effectiveness of the proposed model, we compared its performance with those of models of regression and support vector regression (SVR) under the same conditions, with respect to the prediction of pollen concentrations, risk levels, and season length.
RESULTS
The mean absolute percentage error in the estimated pollen concentrations was 11.18%, 10.37%, and 5.04% for the regression, SVR and DNN models, respectively. The start of the pollen season was estimated to be 20, 22, and 6 days earlier than that predicted by the regression, SVR and DNN models, respectively. Similarly, the end of the pollen season was estimated to be 33, 20, and 9 days later that predicted by the regression, SVR and DNN models, respectively.
CONCLUSIONS
Overall, the DNN model performed better than the other models. However, the prediction of peak pollen concentrations needs improvement. Improved observation quality with optimization of the DNN model will resolve this issue.

Keyword

Pollen; pollen grains; deep learning; quercus; seasons; allergic rhinitis

MeSH Terms

Korea*
Pollen*
Quercus
Rhinitis, Allergic
Rhinitis, Allergic, Seasonal*
Seasons
Trees
Weather

Figure

  • Fig. 1 Structure of DNN model for oak pollen concentration modeling. DNN, deep neural network; WGDD, growing degree day fit to Weibull probability density function; dGDD, difference of growing degree day; Tmax, daily maximum air temperature; Tmin, daily minimum air temperature; PR, daily total precipitation; RH, daily mean relative humidity; WS, daily mean wind speed; Jday, Julian day (number of days from 1 January).

  • Fig. 2 Predicted and observed daily oak pollen concentrations during the evaluation period in 2015–2016 at the 9 sites in Korea. OBS, observation; SVR, support vector regression; DNN, deep neural network.

  • Fig. 3 Comparison of observed (gray area) and predicted pollen seasons by the regression (yellow), SVR (blue) and DNN (red) models at the 9 sites in Korea from 2015 to 2016. OBS, observation; SVR, support vector regression; DNN, deep neural network.


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