PM_(2.5) Forecast System by Using Machine Learning and WRF Model, A Case Study: Ho Chi Minh City, Vietnam

Vo Thi Tam Minh ； Tran Trung Tin ； To Thi Hien

《Aerosol and Air Quality Research》 21卷12期 (2021/12) Pp. 1-17+ap1-4-013

https://doi.org/10.4209/aaqr.210108

摘要

Predicting has necessary implications as part of air pollution alerts and the air quality management system. In recent years, air quality studies and observations in Vietnam have shown that pollution is increasing, especially the concentration of PM_(2.5). There are warnings about excessively high concentrations of PM_(2.5) in the two major cities of Vietnam as Ho Chi Minh City and Hanoi. Projections for PM_(2.5) concentrations in these cities will provide short-term predictive data on air quality. Using the WRF model to forecast PM_(2.5) in Ho Chi Minh City is new research for providing forecast information on air pollution. Experiments with six machine learning algorithms show that the Extra Trees Regression model gives the best forecast with statistical evaluation indicators including RMSE = 7.68 μg m^(-3), MAE = 5.38 μg m^(-3), R-squared = 0.68, and the confusion matrix accuracy of 74%. The experimental setting of the Extra Trees Regression algorithm to predict PM_(2.5) for the next two days with WRF's simulated meteorological data compared with the forecast with observed data showing high accuracy of over 80%. The results show that machine learning with the WRF model can predict PM_(2.5) concentration, suitable for early warning of pollution and information provision for air quality management system in large cities as Ho Chi Minh City.

關鍵字

Machine learning ； Extra Trees Regression ； WRF ； Predict PM_(2.5) ； Ho Chi Minh City