Feature selection for global tropospheric ozone prediction based on the BO-XGBoost-RFE algorithm.

ABSTRACT: Ozone is one of the most important air pollutants, with significant impacts on human health, regional air quality and ecosystems. In this study, we use geographic information and environmental information of the monitoring site of 5577 regions in the world from 2010 to 2014 as feature input to predict the long-term average ozone concentration of the site. A Bayesian optimization-based XGBoost-RFE feature selection model BO-XGBoost-RFE is proposed, and a variety of machine learning algorithms are used to predict ozone concentration based on the optimal feature subset. Since the selection of the underlying model hyperparameters is involved in the recursive feature selection process, different hyperparameter combinations will lead to differences in the feature subsets selected by the model, s

SUBMITTER: Zhang B 

PROVIDER: S-EPMC9163069 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature