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http://dx.doi.org/10.11108/kagis.2022.25.3.074

Improved Estimation of Hourly Surface Ozone Concentrations using Stacking Ensemble-based Spatial Interpolation  

KIM, Ye-Jin (Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
KANG, Eun-Jin (Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
CHO, Dong-Jin (Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
LEE, Si-Woo (Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
IM, Jung-Ho (Dept. of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.25, no.3, 2022 , pp. 74-99 More about this Journal
Abstract
Surface ozone is produced by photochemical reactions of nitrogen oxides(NOx) and volatile organic compounds(VOCs) emitted from vehicles and industrial sites, adversely affecting vegetation and the human body. In South Korea, ozone is monitored in real-time at stations(i.e., point measurements), but it is difficult to monitor and analyze its continuous spatial distribution. In this study, surface ozone concentrations were interpolated to have a spatial resolution of 1.5km every hour using the stacking ensemble technique, followed by a 5-fold cross-validation. Base models for the stacking ensemble were cokriging, multi-linear regression(MLR), random forest(RF), and support vector regression(SVR), while MLR was used as the meta model, having all base model results as additional input variables. The results showed that the stacking ensemble model yielded the better performance than the individual base models, resulting in an averaged R of 0.76 and RMSE of 0.0065ppm during the study period of 2020. The surface ozone concentration distribution generated by the stacking ensemble model had a wider range with a spatial pattern similar with terrain and urbanization variables, compared to those by the base models. Not only should the proposed model be capable of producing the hourly spatial distribution of ozone, but it should also be highly applicable for calculating the daily maximum 8-hour ozone concentrations.
Keywords
Surface ozone; Spatial interpolation; Stacking ensemble; Machine learning; Cokriging;
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Times Cited By KSCI : 10  (Citation Analysis)
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