[Prediction of Autumn Ozone Concentration in the Pearl River Delta Based on Machine Learning].
Huan Jing Ke Xue
; 45(1): 1-7, 2024 Jan 08.
Article
en Zh
| MEDLINE
| ID: mdl-38216453
ABSTRACT
Based on the observation data of the daily maximum 8-hour ozone (O3) average concentration[MDA8-O3, ρ(O3-8h)] and meteorological reanalysis data in the Pearl River Delta Region from 2015 to 2022, four machine learning methods, i.e., support vector machine regression (SVR), random forest (RF), multi-layer perceptron (MLP), and lightweight gradient boosting machine (LG) were used to establish MDA8-O3 prediction models. The results showed that the SVR model had the best prediction performance on MDA8-O3 during the whole year, the coefficient of determination (R2) reached 0.86, and the root mean square error (RMSE) and mean absolute error (MAE) were 16.3 µg·m-3 and 12.3 µg·m-3, respectively. The prediction performance of the SVR model in autumn was still slightly better than that of LG and MLP, with R2,RMSE,and MAE values of 0.88, 19.8 µg·m-3,and 16.1 µg·m-3, respectively. The RF model performed the worst in the autumn prediction. In addition, the models trained by data from the whole year had better prediction ability on autumn MDA8-O3 than that of those only trained by autumn data, and the R2 differed 0.08-0.14.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
Zh
Revista:
Huan Jing Ke Xue
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
China