基于机器学习分类算法的臭氧浓度等级预报 在长沙的应用.

In order to accurately predict the ozone concentration level, this paper used the EC_ Thin global weather model products, global numerical weather prediction products from GRAPES_GFS, which are the graphs independently developed in China, and South China GRACEs atmospheric composition model output products to integrate meteorological and environmental observation data, and used six machine learning intelligent algorithms to build a hybrid model that coupled numerical prediction models with machine learning, in order to give full play to the advantages, complementarity and synergy of numerical prediction and machine learning intelligent algorithms, and improve the accuracy of ozone concentration level prediction by leaps and bounds. Four control experiments were set up for which different characteristic products were selected and the classical classification algorithm of machine learning was used to classify and predict the ozone concentration level in Changsha for any four days to come. The model output with the highest test accuracy was taken as the result statistics. It is found that the test accuracy of the optimal model in 1 to 4 days is 81.7%, 81.7%, 78.3% and 60.9% respectively, which is much higher than the atmospheric composition model prediction and forecaster experience, and achieves the expected design goal. The contribution of high-quality weather model products to product quality is large while that of atmospheric composition model products is limited. The prediction performance of the model within 3 days is good, and low-level prediction performance is good, and high-level prediction performance is general. The following solutions for discussion are proposed: the number of high-level samples is to be increased; the recognition ability of the model is to be enhanced for such events; the mechanism analysis of high-grade ozone pollution is to be strengthened; and more refined factors for the use of the model are to be combined. [ABSTRACT FROM AUTHOR]