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- Title
Using Machine Learning Methods to Forecast Air Quality: A Case Study in Macao.
- Authors
Lei, Thomas M. T.; Siu, Shirley W. I.; Monjardino, Joana; Mendes, Luisa; Ferreira, Francisco
- Abstract
Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.
- Subjects
MACAU (China : Special Administrative Region); MACHINE learning; PARTICULATE matter; RANDOM forest algorithms; COVID-19; COVID-19 pandemic; AIR quality; COMMUNITIES; AIR pollution
- Publication
Atmosphere, 2022, Vol 13, Issue 9, p1412
- ISSN
2073-4433
- Publication type
Article
- DOI
10.3390/atmos13091412