Deep Neural Network-Based Concentration Model for Oak Pollen Allergy Warning in South Korea
10.4168/aair.2020.12.1.149
- Author:
Yun Am SEO
1
;
Kyu Rang KIM
;
Changbum CHO
;
Jae Won OH
;
Tae Hee KIM
Author Information
1. AI Weather Forecast Research Team, National Institute of Meteorological Science, Seogwipo, Korea.
- Publication Type:Original Article
- Keywords:
Pollen;
pollen grains;
deep learning;
quercus;
seasons;
allergic rhinitis
- MeSH:
Korea;
Pollen;
Quercus;
Rhinitis, Allergic;
Rhinitis, Allergic, Seasonal;
Seasons;
Trees;
Weather
- From:Allergy, Asthma & Immunology Research
2020;12(1):149-163
- CountryRepublic of Korea
- Language:English
-
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.
