首页|Study Results from University of Jinan in the Area of Machine Learning Reported (Ozone Concentration Estimation and Meteorological Impact Quantification In the Beijing-tianjin-hebei Region Based On Machine Learning Models)
Study Results from University of Jinan in the Area of Machine Learning Reported (Ozone Concentration Estimation and Meteorological Impact Quantification In the Beijing-tianjin-hebei Region Based On Machine Learning Models)
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NETL
NSTL
Amer Geophysical Union
Fresh data on Machine Learning are presented in a new report. According to news reporting originating from Guangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Accurate estimation of ozone (O3) concentrations and quantitative meteorological contribution are crucial for effective control of O3 pollution. In recent years, there has been a growing interest in leveraging machine learning for O3 pollution research due to its advantages, such as high accuracy, strong generalization, and ease of use.” Financial supporters for this research include National Natural Science Foundation of China, Guangzhou Municipal Science and Technology Project, Special Fund Project for Science and Technology Innovation Strategy of Guangdong Province, Guangdong Provincial Introduction of Innovative Research and Development Team. Our news editors obtained a quote from the research from the University of Jinan, “In this study, we utilized meteorological parameters obtained from european center for medium-range weather forecasts (EMCWF) Reanalysis v5 data as input and employed five distinct machine learning methods to estimate values of maximum daily 8-hr average (MDA8) O3 concentrations and analyze meteorological contributions. To improve the accuracy and generalization capabilities of the estimation, we employed Grid SearchCV techniques to select optimal parameters and mitigate the risk of overfitting. Additionally, we incorporated meteorological normalization and the SHAP model to quantify the influence of various parameters. Among the models evaluated, the Extreme Gradient Boost model exhibited exceptional performance from 2015 to 2022, yielding determination coefficients of 0.85 and 0.80 for the training and test data sets, respectively. The outcomes of meteorological normalization revealed that meteorological parameters accounted for 87.7% of the impacts in 2018, while emission-related factors constituted 80.8% of the impacts in 2021. Over the period spanning 2015-2022, 2 m temperature emerged as the most influential parameter affecting daily MDA8 O3 concentration, with an average contribution of 9.4 mu g m-3.”
GuangzhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningOzoneUniversity of Jinan
NETL
NSTL
Amer Geophysical Union
