Simulation of ground-level ozone concentration in China based on a machine learning algorithm
Objective To explore a high-precision simulation method for ground-level ozone concentration in China based on mul-tiple machine learning models.Methods Based on multi-source data from 2013 to 2017,a national ground-level ozone concentration simulation model was established using multiple machine learning algorithms.Results The random forest(RF)model had the best performance with an R2 of 0.752,and RMSE and MAE of 23.264 μg/m3 and 16.094 μg/m3,respectively.The surface downwelling shortwave radiation was the most critical factor for ground-level ozone concentration simulation.Conclusion The RF model based on multivariate variables such as meteorology,geography,and emission can realize high-precision simulation of ground-level ozone.In the future,the natural source emission data of air pollutants can be further introduced to improve the accuracy of the model.
ground-level ozonesimulationmachine learning algorithmmulti-source data
万方数据
维普
